feat: Add allow_local_network option for self-hosted LLM proxies

Add per-instance boolean option to allow private IP endpoints and HTTP
scheme for self-hosted LLM proxies (LiteLLM, Ollama, vLLM, etc.).

- New CONF_ALLOW_LOCAL_NETWORK config option (default: false)
- When enabled: allows RFC1918 private IPs and HTTP endpoints
- When disabled: full SSRF protection preserved (HTTPS + public IPs only)
- Multicast and unspecified addresses blocked regardless of setting
- Warning logged when local network mode is active
- Checkbox added to ConfigFlow and OptionsFlow UI
- Translations for all 8 languages

Closes #9

.github/ISSUE_TEMPLATE/bug_report.md

@@ -3,7 +3,7 @@ name: Bug report
 about: Create a report to help us improve
 title: ''
 labels: bug
-assignees: ''
+assignees: '' 
 
 ---
 

.github/ISSUE_TEMPLATE/feature_request.md

@@ -3,7 +3,7 @@ name: Feature request
 about: Suggest an idea for this project
 title: ''
 labels: enhancement
-assignees: ''
+assignees: '' 
 
 ---
 

.github/workflows/hassfest.yaml

@@ -1,5 +1,6 @@
 name: Validate with hassfest
-
+permissions:
+  contents: read
 on:
   push:
     branches:

.github/workflows/release.yml

@@ -0,0 +1,37 @@
+name: Release
+
+on:
+  release:
+    types: [created]
+
+permissions:
+  contents: write
+
+jobs:
+  build:
+    name: Build and upload release asset
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ github.event.release.tag_name }}
+
+      - name: Create zip archive
+        run: |
+          cd custom_components
+          zip -r ../ha_text_ai.zip ha_text_ai \
+            -x "ha_text_ai/__pycache__/*" \
+            -x "*.pyc" \
+            -x "*.pyo" \
+            -x "*/__pycache__/*" \
+            -x "*.DS_Store"
+
+      - name: Upload release asset
+        uses: softprops/action-gh-release@v2
+        with:
+          tag_name: ${{ github.event.release.tag_name }}
+          files: ha_text_ai.zip
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/validate.yaml

@@ -1,4 +1,6 @@
 name: Validate
+permissions:
+  contents: read
 
 on:
   push:

.gitignore

@@ -1,39 +1,32 @@
 # Python
 __pycache__/
 *.py[cod]
-*$py.class
-*.so
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
 *.egg-info/
-.installed.cfg
+dist/
+build/
 *.egg
 
-# Home Assistant
-.storage
-.cloud
-.google.token
+# Virtual environments
+.venv/
+venv/
 
 # IDE
 .idea/
 .vscode/
 *.swp
 *.swo
-*~
 
 # OS
 .DS_Store
 Thumbs.db
-*.psd
+
+# Archives
 *.zip
+
+# Home Assistant
+.storage/
+
+# Claude Code working files
+CLAUDE.md
+docs/specs/
+docs/superpowers/

CODE_OF_CONDUCT.md

@@ -0,0 +1,128 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email
+  address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+issue tracker.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.

CONTRIBUTING.md

@@ -21,7 +21,7 @@ We welcome contributions to the HA Text AI project! This document will help you
    git remote add upstream https://github.com/smkrv/ha-text-ai.git
    ```
 
-### 2. Creating a Development Branch
+### 2. Creating a Development Branch 
 
 ```bash
 # Update the main branch

LICENSE

@@ -1,22 +1,131 @@
-MIT License
+# PolyForm Noncommercial License 1.0.0
 
-Copyright (c) 2024 smkrv
+<https://polyformproject.org/licenses/noncommercial/1.0.0>
 
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
+## Acceptance
 
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
+In order to get any license under these terms, you must agree
+to them as both strict obligations and conditions to all
+your licenses.
 
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
- 
+## Copyright License
+
+The licensor grants you a copyright license for the
+software to do everything you might do with the software
+that would otherwise infringe the licensor's copyright
+in it for any permitted purpose.  However, you may
+only distribute the software according to [Distribution
+License](#distribution-license) and make changes or new works
+based on the software according to [Changes and New Works
+License](#changes-and-new-works-license).
+
+## Distribution License
+
+The licensor grants you an additional copyright license
+to distribute copies of the software.  Your license
+to distribute covers distributing the software with
+changes and new works permitted by [Changes and New Works
+License](#changes-and-new-works-license).
+
+## Notices
+
+You must ensure that anyone who gets a copy of any part of
+the software from you also gets a copy of these terms or the
+URL for them above, as well as copies of any plain-text lines
+beginning with `Required Notice:` that the licensor provided
+with the software.  For example:
+
+> Required Notice: Copyright Yoyodyne, Inc. (http://example.com)
+
+## Changes and New Works License
+
+The licensor grants you an additional copyright license to
+make changes and new works based on the software for any
+permitted purpose.
+
+## Patent License
+
+The licensor grants you a patent license for the software that
+covers patent claims the licensor can license, or becomes able
+to license, that you would infringe by using the software.
+
+## Noncommercial Purposes
+
+Any noncommercial purpose is a permitted purpose.
+
+## Personal Uses
+
+Personal use for research, experiment, and testing for
+the benefit of public knowledge, personal study, private
+entertainment, hobby projects, amateur pursuits, or religious
+observance, without any anticipated commercial application,
+is use for a permitted purpose.
+
+## Noncommercial Organizations
+
+Use by any charitable organization, educational institution,
+public research organization, public safety or health
+organization, environmental protection organization,
+or government institution is use for a permitted purpose
+regardless of the source of funding or obligations resulting
+from the funding.
+
+## Fair Use
+
+You may have "fair use" rights for the software under the
+law. These terms do not limit them.
+
+## No Other Rights
+
+These terms do not allow you to sublicense or transfer any of
+your licenses to anyone else, or prevent the licensor from
+granting licenses to anyone else.  These terms do not imply
+any other licenses.
+
+## Patent Defense
+
+If you make any written claim that the software infringes or
+contributes to infringement of any patent, your patent license
+for the software granted under these terms ends immediately. If
+your company makes such a claim, your patent license ends
+immediately for work on behalf of your company.
+
+## Violations
+
+The first time you are notified in writing that you have
+violated any of these terms, or done anything with the software
+not covered by your licenses, your licenses can nonetheless
+continue if you come into full compliance with these terms,
+and take practical steps to correct past violations, within
+32 days of receiving notice.  Otherwise, all your licenses
+end immediately.
+
+## No Liability
+
+***As far as the law allows, the software comes as is, without
+any warranty or condition, and the licensor will not be liable
+to you for any damages arising out of these terms or the use
+or nature of the software, under any kind of legal claim.***
+
+## Definitions
+
+The **licensor** is the individual or entity offering these
+terms, and the **software** is the software the licensor makes
+available under these terms.
+
+**You** refers to the individual or entity agreeing to these
+terms.
+
+**Your company** is any legal entity, sole proprietorship,
+or other kind of organization that you work for, plus all
+organizations that have control over, are under the control of,
+or are under common control with that organization.  **Control**
+means ownership of substantially all the assets of an entity,
+or the power to direct its management and policies by vote,
+contract, or otherwise.  Control can be direct or indirect.
+
+**Your licenses** are all the licenses granted to you for the
+software under these terms.
+
+**Use** means anything you do with the software requiring one
+of your licenses.

README.md

@@ -2,137 +2,207 @@
 
 <div align="center">  
 
-![GitHub release](https://img.shields.io/github/release/smkrv/ha-text-ai.svg?style=flat-square) ![GitHub last commit](https://img.shields.io/github/last-commit/smkrv/ha-text-ai.svg?style=flat-square) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=flat-square)](https://opensource.org/licenses/MIT) [![hacs_badge](https://img.shields.io/badge/HACS-Custom-41BDF5.svg?style=flat-square)](https://github.com/hacs/integration) ![/README.md](https://img.shields.io/badge/language-English-green?style=flat-square) ![/README_RU.md](https://img.shields.io/badge/language-Russian-green?style=flat-square) ![](https://img.shields.io/badge/language-Deutch-green?style=flat-square)
+  ![GitHub release](https://img.shields.io/github/v/release/smkrv/ha-text-ai?style=flat-square) ![GitHub last commit](https://img.shields.io/github/last-commit/smkrv/ha-text-ai?style=flat-square) [![License: PolyForm Noncommercial](https://img.shields.io/badge/License-PolyForm%20Noncommercial%201.0.0-lightgrey.svg?style=flat-square)](https://polyformproject.org/licenses/noncommercial/1.0.0) [![hacs_badge](https://img.shields.io/badge/HACS-Default-41BDF5.svg?style=flat-square)](https://github.com/hacs/integration)
+  ![Deutsch](https://img.shields.io/badge/lang-DE-blue?style=flat-square) ![English](https://img.shields.io/badge/lang-EN-blue?style=flat-square) ![Español](https://img.shields.io/badge/lang-ES-blue?style=flat-square) ![हिन्दी](https://img.shields.io/badge/lang-HI-blue?style=flat-square) ![Italiano](https://img.shields.io/badge/lang-IT-blue?style=flat-square) ![Русский](https://img.shields.io/badge/lang-RU-blue?style=flat-square) ![Српски](https://img.shields.io/badge/lang-SR-blue?style=flat-square) ![中文](https://img.shields.io/badge/lang-ZH-blue?style=flat-square)
 
-  <img src="https://github.com/smkrv/ha-text-ai/blob/524849f6a945ec62c2cf6a6b7ecd9a28b37bf0fa/misc/icons/logo.jpg" alt="HA Text AI" height="160"/>
 
-### Advanced AI Integration for Home Assistant with LLM multi-provider support
+  <img src="https://github.com/smkrv/ha-text-ai/blob/main/custom_components/ha_text_ai/icons/logo%402x.png" alt="HA Text AI" style="width: 50%; max-width: 256px; max-height: 128px; aspect-ratio: 2/1; object-fit: contain;"/>
+
+### Advanced AI Integration for [Home Assistant](https://www.home-assistant.io/) with LLM multi-provider support
 </div>
 
 <p align="center">
-Transform your smart home experience with powerful AI assistance powered by multiple AI providers including OpenAI GPT and Anthropic Claude models. Get intelligent responses, automate complex scenarios, and enhance your home automation with advanced natural language processing.
+Transform your smart home experience with powerful AI assistance powered by multiple AI providers including OpenAI GPT, Anthropic Claude, DeepSeek and Google Gemini models. Get intelligent responses, automate complex scenarios, and enhance your home automation with advanced natural language processing.
 
 </p>
 
 ---
 
 > [!IMPORTANT]
-> 🚧 ALPHA VERSION 🚧  
-> Expect: potential bugs, frequent changes, incomplete features.  
-> 🤝 Community Driven  
+> 🤝 Community Driven: for more details on the integration,   
+> check out the discussion on the **[Home Assistant Community forum](https://community.home-assistant.io/t/ha-text-ai-transforming-home-automation-through-multi-llm-integration/799741)**
 >
->  <a href="https://community.home-assistant.io/t/ha-text-ai-transforming-home-automation-with-multi-provider-language-models/799741"><img src="https://img.shields.io/badge/Community-blue?style=for-the-badge&logo=homeassistant&logoColor=white&color=03a9f4"/></a>  
+>  <a href="https://my.home-assistant.io/redirect/hacs_repository/?owner=smkrv&repository=ha-text-ai&category=Integration"><img src="https://my.home-assistant.io/badges/hacs_repository.svg" width="210" height="auto"></a>
+>  
+> [Screenshots](assets/images/screenshots/screenshot.jpg)
 
 ## 🌟 Features
 
-- 🧠 **Multi-Provider AI Integration**:
-  - Support for OpenAI GPT models
-  - Anthropic Claude integration
-  - Custom API endpoints
-  - Flexible model selection
+- 🧠 **Multi-Provider AI Integration**: Support for OpenAI GPT, Anthropic Claude, DeepSeek and Google Gemini models
+- 💬 **Advanced Language Processing**: Context-aware, multi-turn conversations
+- 📝 **Enhanced Memory Management**: Secure file-based history storage
+- ⚡ **Performance Optimization**: Efficient token usage and smart rate limiting
+- 🎯 **Advanced Customization**: Per-request model and parameter selection
+- 🔒 **Enhanced Security**: Secure API key management and usage monitoring
+- 🎨 **Improved User Experience**: Intuitive configuration and rich interfaces
+- 🔄 **Automation Integration**: Event-driven responses and template compatibility
 
-- 💬 **Advanced Language Processing**:
-  - Context-aware responses
-  - Multi-turn conversations
-  - Custom system instructions
-  - Natural conversation flow
+<details>
+<summary>📦 Detailed Feature Breakdown</summary>
 
-- 📝 **Enhanced Memory Management**:
-  - Persistent conversation history
-  - Context-aware responses
-  - Customizable history limits
-  - Model-specific filtering
+### 🧠 **Multi-Provider AI Integration**
+- Support for OpenAI GPT models
+- Anthropic Claude integration
+- DeepSeek integration
+- Google Gemini integration
+- Custom API endpoints
+- Flexible model selection
 
-- ⚡ **Performance Optimization**:
-  - Efficient token usage
-  - Smart rate limiting
-  - Response caching
-  - Request interval control
+### 💬 **Advanced Language Processing**
+- Context-aware responses
+- Multi-turn conversations
+- Custom system instructions
+- Natural conversation flow
 
-- 🎯 **Advanced Customization**:
-  - Per-request model selection
-  - Adjustable parameters
-  - Custom system prompts
-  - Temperature control
+### 📝 **Enhanced Memory Management**
+- File-based conversation history storage
+- Automatic history rotation
+- Configurable history size limits
+- Secure storage in Home Assistant
 
-- 🔒 **Enhanced Security**:
-  - Secure API key storage
-  - Rate limiting protection
-  - Error handling
-  - Usage monitoring
+### ⚡ **Performance Optimization**
+- Efficient token usage
+- Smart rate limiting
+- Response caching
+- Request interval control
 
-- 🎨 **Improved User Experience**:
-  - Intuitive configuration UI
-  - Detailed sensor attributes
-  - Rich service interface
-  - Model selection UI
+### 🎯 **Advanced Customization**
+- Per-request model selection
+- Adjustable parameters
+- Custom system prompts
+- Temperature control
 
-- 🔄 **Automation Integration**:
-  - Event-driven responses
-  - Conditional logic support
-  - Template compatibility
-  - Model-specific automation
+### 🔒 **Enhanced Security**
+- Secure API key storage
+- Rate limiting protection
+- Error handling
+- Usage monitoring
+
+### 🎨 **Improved User Experience**
+- Intuitive configuration UI
+- Detailed sensor attributes
+- Rich service interface
+- Model selection UI
+
+### 🔄 **Automation Integration**
+- Event-driven responses
+- Conditional logic support
+- Template compatibility
+- Model-specific automation
+
+</details>
+
+#### 🌐 Translations
+
+| Code | Language | Status |
+|------|----------|--------|
+| 🇩🇪 de | Deutsch | Full |
+| 🇬🇧 en | English | Primary |
+| 🇪🇸 es | Español | Full |
+| 🇮🇳 hi | हिन्दी | Full |
+| 🇮🇹 it | Italiano | Full |
+| 🇷🇺 ru | Русский | Full |
+| 🇷🇸 sr | Српски | Full |
+| 🇨🇳 zh | 中文 | Full |
 
 ## 📋 Prerequisites
 
-- Home Assistant 2024.11 or later
+- Home Assistant 2024.12.0 or later (recommended for best compatibility)
 - Active API key from:
   - OpenAI ([Get key](https://platform.openai.com/account/api-keys))
   - Anthropic ([Get key](https://console.anthropic.com/))
+  - DeepSeek ([Get key](https://platform.deepseek.com/api_keys))
   - OpenRouter ([Get key](https://openrouter.ai/keys))
+  - Google Gemini 🆕 ([Get key](https://ai.google.dev/gemini-api/docs/api-key)) thanks to ([@Azzedde](https://github.com/Azzedde))
+  - Any OpenAI-compatible API provider
 - Python 3.9 or newer
 - Stable internet connection
 
-### Configuration Options
-- API Provider (OpenAI/Anthropic)
-- API Key (provider-specific)
-- Model Selection (flexible, provider-specific models)
-- Temperature (Creativity control, 0.0-2.0)
-- Max Tokens (Response length limit)
-- Request Interval (API call throttling)
-- Custom API Endpoint (optional)
+## Configuration Options
 
-#### ⓘ Potentially Compatible Providers  
-The integration is designed to be flexible and may work with other providers offering OpenAI-compatible APIs:  
-- Groq  
-- Together AI  
-- Perplexity AI  
-- Mistral AI  
-- Google AI  
-- Local AI servers (like Ollama)  
-- Custom OpenAI-compatible endpoints  
+### 🔧 **Core Configuration Settings**
+- 🌐 **API Provider**: OpenAI/Anthropic/DeepSeek/Gemini
+- 🔑 **API Key**: Provider-specific authentication
+- 🤖 **Model Selection**: Flexible, provider-specific models
+- 🌡️ **Temperature**: Creativity control (0.0-2.0)
+- 📏 **Max Tokens**: Response length limit (passed directly to the LLM API to control the maximum length of the response)
+- ⏱️ **Request Interval**: API call throttling
+- 💾 **History Size**: Number of messages to retain
+- 🌍 **Custom API Endpoint**: Optional advanced configuration
 
-#### Additional Notes  
-- Not all providers guarantee full compatibility  
-- Performance may vary between providers  
-- Check individual provider's documentation  
+### 🤖 **Recommended Models**
+
+#### OpenAI Models
+- **GPT-5** - The latest flagship model, best for complex reasoning
+- **GPT-5 mini** - A cost-effective and fast model, suitable for most tasks
+
+#### Anthropic Claude Models
+- **Claude Opus 4.6** - The most capable model for handling complex tasks
+- **Claude Sonnet 4.6** - Offers a balance between performance and cost
+- **Claude Haiku 4.5** - The fastest and most economical option in the series
+
+#### DeepSeek Models
+- **DeepSeek-V3** - A general-purpose model for a wide range of tasks
+- **DeepSeek-R1** - A specialized model focused on reasoning and coding
+
+#### Google Gemini Models
+- **Gemini 3.1 Pro** - The newest and most advanced model available
+- **Gemini 3.1 Flash Lite** - Fastest and most cost-efficient model for high-volume workloads
+
+<details>
+<summary>🌐 Potentially Compatible Providers</summary>
+
+#### Flexible Provider Ecosystem
+The integration is designed to be flexible and may work with other providers offering OpenAI-compatible APIs:
+- Groq
+- Together AI
+- Perplexity AI
+- Mistral AI
+- Google AI
+- Local AI servers (like Ollama)
+- Custom OpenAI-compatible endpoints
+
+#### 🚨 Compatibility Notes
+- Not all providers guarantee full compatibility
+- Performance may vary between providers
+- Check individual provider's documentation
 - Ensure your API key has sufficient credits/quota
 
-#### Provider Compatibility Requirements  
-To be compatible, a provider should support:  
-- OpenAI-like REST API structure  
-- JSON request/response format  
-- Standard authentication method  
+#### 🔍 Provider Compatibility Requirements
+To be compatible, a provider should support:
+- OpenAI-like REST API structure
+- JSON request/response format
+- Standard authentication method
 - Similar model parameter handling
 
+</details>
+
 ## ⚡ Installation
 
 ### HACS Installation (Recommended)
+>[!TIP]
+>HA Text AI is available in the default HACS repository. You can install it directly through HACS or click the button below to open it there.
+
 <a href="https://my.home-assistant.io/redirect/hacs_repository/?owner=smkrv&repository=ha-text-ai&category=Integration"><img src="https://my.home-assistant.io/badges/hacs_repository.svg" width="170" height="auto"></a>
 1. Open HACS in Home Assistant
 2. Click on "Integrations"
-3. Click "..." in top right corner
-4. Select "Custom repositories"
-5. Add repository URL: `https://github.com/smkrv/ha-text-ai`
-6. Choose "Integration" as category
-7. Click "Download"
-8. Restart Home Assistant
+3. Search for "HA Text AI"
+4. Click "Download"
+5. Restart Home Assistant
+
+**Alternative Method (Custom Repository):**
+If the integration is not found in the default repository:
+1. Click "..." in top right corner of HACS
+2. Select "Custom repositories"
+3. Add repository URL: `https://github.com/smkrv/ha-text-ai`
+4. Choose "Integration" as category
+5. Click "Download"
 
 ### Manual Installation
 1. Download the latest release
 2. Extract and copy `custom_components/ha_text_ai` to your `custom_components` directory
 3. Restart Home Assistant
-4. Add configuration via UI or YAML
+4. Add configuration via UI (Settings → Devices & Services → Add Integration)
 
 ## ⚙️ Configuration
 
@@ -142,80 +212,54 @@ To be compatible, a provider should support:
 3. Search for "HA Text AI"
 4. Follow the configuration steps
 
-### Via YAML
-
-### Platform Configuration (Global Settings)
-
-```yaml
-ha_text_ai:
-  api_provider: openai  # Required
-  api_key: !secret ai_api_key  # Required
-  model: gpt-4o-mini  # Strongly recommended
-  temperature: 0.7  # Optional
-  max_tokens: 1000  # Optional
-  request_interval: 1.0  # Optional
-  api_endpoint: https://api.openai.com/v1  # Required
-  system_prompt: |  # Optional
-    You are a home automation expert assistant.
-    Focus on practical and efficient solutions.
-```
-
-### Sensor Configuration
-
-```yaml
-sensor:
-  - platform: ha_text_ai
-    name: "My AI Assistant"  # Required, unique identifier
-    api_provider: openai  # Optional (inherits from platform)
-    model: "gpt-4o-mini"  # Optional
-    temperature: 0.7  # Optional
-    max_tokens: 1000  # Optional
-```
-
-### 📋 Configuration Parameters
-
-#### Platform Configuration
-
-| Parameter | Type | Required | Default | Description |
-|-----------|------|----------|---------|-------------|
-| `api_provider` | String | ✅ | - | AI service provider (openai, anthropic) |
-| `api_key` | String | ✅ | - | Authentication key for AI service |
-| `model` | String | ⚠️ | Provider default | Strongly recommended: Specific AI model to use. If not specified, the provider's default model will be used |
-| `temperature` | Float | ❌ | 0.7 | Response creativity level (0.0-2.0) |
-| `max_tokens` | Integer | ❌ | 1000 | Maximum response length |
-| `request_interval` | Float | ❌ | 1.0 | Delay between API requests |
-| `api_endpoint` | URL | ⚠️ | Provider default | Custom API endpoint |
-| `system_prompt` | String | ❌ | - | Default context for AI interactions |
-
-#### Sensor Configuration
-
-| Parameter | Type | Required | Default | Description |
-|-----------|------|----------|---------|-------------|
-| `platform` | String | ✅ | - | Must be `ha_text_ai` |
-| `name` | String | ✅ | - | Unique sensor identifier |
-| `api_provider` | String | ❌ | Platform setting | Override global provider |
-| `model` | String | ⚠️ | Platform setting | Recommended: Override global model. If not specified, uses platform or provider default |
-| `temperature` | Float | ❌ | Platform setting | Override global temperature |
-| `max_tokens` | Integer | ❌ | Platform setting | Override global max tokens |
+> **Note:** This integration is configured exclusively through the UI (config entries). YAML configuration is not supported.
 
 ## 🛠️ Available Services
 
+### 🔄 Response Variables (New!)
+
+**HA Text AI now supports response variables** - a powerful feature that returns AI responses directly from service calls, eliminating the need for separate text sensors and the 255-character limitation!
+
+#### ✨ Key Benefits:
+- **Unlimited response length** - No more 255-character truncation
+- **Direct data access** - Get responses immediately in automations
+- **Race condition prevention** - Eliminates conflicts in parallel automations
+- **Simplified workflows** - No need to read from sensors
+
 ### ask_question
 ```yaml
 service: ha_text_ai.ask_question
 data:
   question: "What's the optimal temperature for sleeping?"
-  model: "claude-3-sonnet"  # optional
+  model: "claude-sonnet-4-6-20260217"  # optional
   temperature: 0.5  # optional
   max_tokens: 500  # optional
   context_messages: 10  #optional, number of previous messages to include in context, default: 5
   system_prompt: "You are a sleep optimization expert"  # optional
+  instance: sensor.ha_text_ai_gpt
+response_variable: ai_response  # NEW! Store response data directly
+```
+
+#### 📊 Response Data Structure:
+```yaml
+# The service returns structured data:
+response_text: "The optimal sleeping temperature is 65-68°F (18-20°C)..."
+tokens_used: 150
+prompt_tokens: 50
+completion_tokens: 100
+model_used: "claude-sonnet-4-6-20260217"
+instance: "sensor.ha_text_ai_gpt"
+question: "What's the optimal temperature for sleeping?"
+timestamp: "2025-02-09T16:57:00.000Z"
+success: true
+# error: "Error message" (only present if success: false)
 ```
 
 ### set_system_prompt
 ```yaml
 service: ha_text_ai.set_system_prompt
 data:
+  instance: sensor.ha_text_ai_gpt
   prompt: |
     You are a home automation expert focused on:
     1. Energy efficiency
@@ -227,14 +271,163 @@ data:
 ### clear_history
 ```yaml
 service: ha_text_ai.clear_history
+data:
+  instance: sensor.ha_text_ai_gpt
 ```
 
 ### get_history
 ```yaml
 service: ha_text_ai.get_history
 data:
-  limit: 5  # optional
-  filter_model: "gpt-4o"  # optional
+  limit: 5  # optional, number of conversations to return (1-100)
+  filter_model: "gpt-4o"  # optional, filter by specific AI model
+  start_date: "2025-02-01"  # optional, filter conversations from this date
+  include_metadata: false  # optional, include tokens, response time, etc.
+  sort_order: "newest"  # optional, sort order: "newest" or "oldest"
+  instance: sensor.ha_text_ai_gpt
+```
+
+## 🚀 Advanced Automation Examples with Response Variables
+
+### Example 1: Smart Home Advice with Direct Response
+```yaml
+automation:
+  - alias: "Get AI Home Advice"
+    trigger:
+      - platform: state
+        entity_id: input_button.ask_ai_advice
+    action:
+      - service: ha_text_ai.ask_question
+        data:
+          question: "What's the best way to optimize energy usage in my home?"
+          instance: sensor.ha_text_ai_gpt
+        response_variable: ai_advice
+      - service: notify.mobile_app
+        data:
+          title: "🏠 Smart Home Tip"
+          message: |
+            {{ ai_advice.response_text }}
+            
+            📊 Tokens used: {{ ai_advice.tokens_used }}
+            🤖 Model: {{ ai_advice.model_used }}
+```
+
+### Example 2: Weather-Based AI Recommendations
+```yaml
+automation:
+  - alias: "Weather-Based AI Suggestions"
+    trigger:
+      - platform: numeric_state
+        entity_id: sensor.outdoor_temperature
+        below: 0
+    action:
+      - service: ha_text_ai.ask_question
+        data:
+          question: |
+            The outdoor temperature is {{ states('sensor.outdoor_temperature') }}°C. 
+            What should I do to prepare my home for freezing weather?
+          system_prompt: "You are a home maintenance expert. Provide practical, actionable advice."
+          instance: sensor.ha_text_ai_gpt
+        response_variable: winter_advice
+      - if:
+          - condition: template
+            value_template: "{{ winter_advice.success }}"
+        then:
+          - service: persistent_notification.create
+            data:
+              title: "❄️ Winter Preparation Advice"
+              message: |
+                {{ winter_advice.response_text }}
+                
+                Generated at: {{ winter_advice.timestamp }}
+        else:
+          - service: persistent_notification.create
+            data:
+              title: "⚠️ AI Service Error"
+              message: "Failed to get winter advice: {{ winter_advice.error }}"
+```
+
+### Example 3: Multi-Step AI Workflow
+```yaml
+automation:
+  - alias: "Multi-Step AI Analysis"
+    trigger:
+      - platform: state
+        entity_id: input_button.analyze_home_status
+    action:
+      # Step 1: Get current status analysis
+      - service: ha_text_ai.ask_question
+        data:
+          question: |
+            Current home status:
+            - Temperature: {{ states('sensor.indoor_temperature') }}°C
+            - Humidity: {{ states('sensor.indoor_humidity') }}%
+            - Energy usage: {{ states('sensor.power_consumption') }}W
+            
+            Analyze this data and provide insights.
+          instance: sensor.ha_text_ai_gpt
+        response_variable: status_analysis
+      
+      # Step 2: Get recommendations based on analysis
+      - service: ha_text_ai.ask_question
+        data:
+          question: |
+            Based on this analysis: "{{ status_analysis.response_text[:500] }}"
+            
+            Provide 3 specific actionable recommendations for improvement.
+          context_messages: 2  # Include previous conversation
+          instance: sensor.ha_text_ai_gpt
+        response_variable: recommendations
+      
+      # Step 3: Send comprehensive report
+      - service: notify.telegram
+        data:
+          title: "🏠 Home Analysis Report"
+          message: |
+            **Analysis:**
+            {{ status_analysis.response_text }}
+            
+            **Recommendations:**
+            {{ recommendations.response_text }}
+            
+            **Report Details:**
+            - Total tokens used: {{ status_analysis.tokens_used + recommendations.tokens_used }}
+            - Analysis model: {{ status_analysis.model_used }}
+            - Generated: {{ recommendations.timestamp }}
+```
+
+### 💡 Migration from Sensors to Response Variables
+
+#### Old Method (Limited):
+```yaml
+# ❌ Old way - limited to 255 characters, race conditions
+automation:
+  - alias: "Old AI Response Method"
+    action:
+      - service: ha_text_ai.ask_question
+        data:
+          question: "Long question here..."
+          instance: sensor.ha_text_ai_gpt
+      - delay: "00:00:05"  # Wait for sensor update
+      - service: notify.mobile
+        data:
+          message: "{{ state_attr('sensor.ha_text_ai_gpt', 'response')[:255] }}..."  # Truncated!
+```
+
+#### New Method (Unlimited):
+```yaml
+# ✅ New way - unlimited length, immediate access, no race conditions
+automation:
+  - alias: "New AI Response Method"
+    action:
+      - service: ha_text_ai.ask_question
+        data:
+          question: "Long question here..."
+          instance: sensor.ha_text_ai_gpt
+        response_variable: ai_response  # Direct access!
+      - service: notify.mobile
+        data:
+          message: "{{ ai_response.response_text }}"  # Full response, no truncation!
 ```
 
 ### 🏷️ HA Text AI Sensor Naming Convention
@@ -243,7 +436,7 @@ data:
 - Only lowercase letters (a-z)  
 - Numbers (0-9)  
 - Underscore (_)  
-- Maximum length: 50 characters (including `ha_text_ai_` prefix (14 characters)
+- Maximum length: 50 characters (including `ha_text_ai_`)
 
 #### Sensor Name Structure
 ```yaml
@@ -254,7 +447,7 @@ sensor.ha_text_ai_YOUR_UNIQUE_SUFFIX
 # Examples:
 sensor.ha_text_ai_gpt      # GPT-based sensor
 sensor.ha_text_ai_claude   # Claude-based sensor
-sensor.ha_text_ai_gpt   # Custom suffix
+sensor.ha_text_ai_abc      # Custom suffix
 ```
 
 #### Response Retrieval
@@ -271,6 +464,7 @@ automation:
       - service: ha_text_ai.ask_question
         data:
           question: "Home automation advice"
+          instance: sensor.ha_text_ai_gpt
       - service: notify.mobile
         data:
           message: >
@@ -287,6 +481,16 @@ automation:
 
 ### 🔍 HA Text AI Sensor Attributes
 
+- 🤖 **Model and Provider Information**: Tracking current AI model and service provider
+- 🚦 **System Status**: Real-time API and processing readiness
+- 📊 **Performance Metrics**: Request success rates and response times
+- 💬 **Conversation Tracking**: Token usage and interaction history are estimated using a heuristic method based on word count and specific word characteristics, which may differ from actual token usage.
+- 🕒 **Last Interaction Details**: Recent query and response tracking
+- ❤️ **System Health**: Error monitoring and service uptime
+
+<details>
+<summary>📦 Detailed Sensor Attributes</summary>
+
 #### Model and Provider Information
 ```yaml
 # Name of the AI model currently in use (e.g., latest version of GPT)  
@@ -301,10 +505,7 @@ automation:
 
 #### System Status
 ```yaml
-# Current operational readiness of the AI service API  
-{{ state_attr('sensor.ha_text_ai_gpt', 'Api status') }}      # ready  
-
-# Indicates if a request is currently being processed  
+# Indicates if a request is currently being processed
 {{ state_attr('sensor.ha_text_ai_gpt', 'Is processing') }}   # false  
 
 # Shows if the API has hit its request rate limit  
@@ -342,6 +543,12 @@ automation:
 
 # Tokens used in the AI's generated responses  
 {{ state_attr('sensor.ha_text_ai_gpt', 'Completion tokens') }}     # 0
+
+# Number of entries in current history file
+{{ state_attr('sensor.ha_text_ai_gpt', 'History size') }}          # 0  
+
+# Last few conversation entries (last 5 for performance)
+{{ state_attr('sensor.ha_text_ai_gpt', 'conversation_history') }}  # [...]
 ```
 
 #### Last Interaction Details
@@ -368,19 +575,27 @@ automation:
 {{ state_attr('sensor.ha_text_ai_gpt', 'Uptime') }}          # 547,58
 ```
 
+### History Storage
+Conversation history stored in `.storage/ha_text_ai_history/` directory:
+- Each instance has its own history file (JSON)
+- Files are automatically rotated when size limit is reached
+- Archived history files are timestamped
+- Default maximum file size: 1MB
+
 ### 💡 Pro Tips
 - Always check attribute existence
 - Use these attributes for monitoring and automation
 - Some values might be 0 or empty initially
 
+</details>
 
 ## 📘 FAQ
 
 **Q: Which AI providers are supported?**
-A: Currently OpenAI (GPT models) and Anthropic (Claude models) are supported, with more providers planned.
+A: OpenAI (GPT models), Anthropic (Claude models), DeepSeek, Google Gemini, and OpenRouter are officially supported, with many other OpenAI-compatible providers working as well.
 
 **Q: How can I reduce API costs?**
-A: Use GPT-3.5-Turbo or Claude-3-Sonnet for most queries, implement caching, and optimize token usage.
+A: Use gpt-5-mini or claude-haiku-4-5 for most queries, implement caching, and optimize token usage.
 
 **Q: Are there limitations on the number of requests?**
 A: Depends on your API provider's plan. We recommend monitoring usage and implementing request throttling via `request_interval` configuration.
@@ -391,8 +606,11 @@ A: Yes, you can configure custom endpoints and use any compatible model by speci
 **Q: How do I switch between different AI providers?**
 A: Simply change the model parameter in your configuration or service calls to use the desired provider's model.
 
-**Q: How can I reduce API costs?**
-A: Use GPT-3.5-Turbo for most queries, implement caching, and optimize token usage.
+**Q: What are the token limits for different models?**
+A: Token limits vary by provider and model. OpenAI's GPT-5 supports up to 1M context tokens, Claude Opus 4.6 supports up to 1M tokens, Gemini 3.1 Pro supports up to 1M tokens, while smaller models typically have 128K-200K limits. Check your provider's documentation for specific limits.
+
+**Q: How do I monitor token usage?**
+A: Use the sensor attributes like `Total tokens`, `Prompt tokens`, and `Completion tokens` to track usage. You can also create automations to alert you when usage exceeds certain thresholds.
 
 **Q: Is my data secure?**
 A: Yes, your data is secure. The system operates entirely on your local machine, keeping your data under your control. API keys are stored securely and all external communications use encrypted connections.
@@ -400,6 +618,15 @@ A: Yes, your data is secure. The system operates entirely on your local machine,
 **Q: How do context messages work?**
 A: Context messages allow the AI to remember and reference previous conversation history. By default, 5 previous messages are included, but you can customize this from 1 to 20 messages to control the conversation depth and token usage.
 
+**Q: Where is conversation history stored?**  
+A: History is stored in files under the `.storage/ha_text_ai_history/` directory, with automatic rotation and size management.
+
+**Q: Can I access old conversation history?**  
+A: Yes, archived history files are stored with timestamps and can be accessed manually if needed.
+
+**Q: How much history is kept?**  
+A: By default, up to 50 conversations are stored (max 200), configurable via UI. Files are automatically rotated when they reach 1MB.
+
 ## 🤝 Contributing
 
 Contributions welcome! Please read our [Contributing Guide](CONTRIBUTING.md).
@@ -410,9 +637,23 @@ Contributions welcome! Please read our [Contributing Guide](CONTRIBUTING.md).
 4. Push branch (`git push origin feature/Enhancement`)
 5. Open Pull Request
 
+## Legal Disclaimer and Limitation of Liability  
+
+### Software Disclaimer  
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,   
+INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A   
+PARTICULAR PURPOSE AND NONINFRINGEMENT.  
+
+IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,   
+DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,   
+ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER   
+DEALINGS IN THE SOFTWARE.  
+
 ## 📝 License
 
-MIT License - see [LICENSE](LICENSE) for details.
+Author: SMKRV
+[PolyForm Noncommercial 1.0.0](https://polyformproject.org/licenses/noncommercial/1.0.0) - see [LICENSE](LICENSE) for details.
 
 ## 💡 Support the Project
 
@@ -432,10 +673,12 @@ If you want to say thanks financially, you can send a small token of appreciatio
 
 ---
 
+<div align="center"><img src="https://github.com/smkrv/ha-text-ai/blob/2aaf3405759eb2d97624834594e24ace896131df/assets/images/icons/footer_icon.png" alt="HA Text AI" style="width: 128px; height: auto;"/></div>  
 <div align="center">
 
-Made with ❤️ and Claude 3.5 Sonnet for the Home Assistant Community
+Made with ❤️ for the Home Assistant Community
 
 [Report Bug](https://github.com/smkrv/ha-text-ai/issues) · [Request Feature](https://github.com/smkrv/ha-text-ai/issues)
 
 </div>
+ 

RESPONSE_VARIABLE_EXAMPLE.md

@@ -0,0 +1,148 @@
+# Using response_variable with HA Text AI
+
+After updating the HA Text AI integration, it now supports using the `response_variable` parameter in Home Assistant scripts and automations.
+
+## What Changed
+
+- Added response schema support in the `ha_text_ai.ask_question` service
+- Service is now correctly registered with `supports_response=True` flag
+- You can now use `response_variable` to capture AI response in a variable
+
+## Example Usage in Script
+
+```yaml
+action: ha_text_ai.ask_question
+data:
+  context_messages: 0
+  temperature: 0.7
+  max_tokens: 1000
+  instance: sensor.ha_text_ai_gemini
+  question: "What time is it?"
+response_variable: ai_response
+```
+
+## Example Usage in Automation
+
+```yaml
+alias: "Get AI Response"
+trigger:
+  - platform: state
+    entity_id: input_boolean.ask_ai
+    to: "on"
+action:
+  - action: ha_text_ai.ask_question
+    data:
+      instance: sensor.ha_text_ai_gemini
+      question: "What's the current weather?"
+      temperature: 0.7
+      max_tokens: 500
+    response_variable: weather_response
+  
+  - action: notify.persistent_notification
+    data:
+      title: "AI Response"
+      message: "{{ weather_response.response_text }}"
+```
+
+## Available Fields in response_variable
+
+When you use `response_variable`, you will receive an object with the following fields:
+
+- `response_text` (string) - The AI response text
+- `tokens_used` (integer) - Total number of tokens used
+- `prompt_tokens` (integer) - Number of tokens in the prompt
+- `completion_tokens` (integer) - Number of tokens in the completion
+- `model_used` (string) - The AI model that was used for the response
+- `instance` (string) - The instance name that was used
+- `question` (string) - The original question that was asked
+- `timestamp` (string) - ISO timestamp when the response was generated
+- `success` (boolean) - Whether the request was successful
+- `error` (string) - Error message if the request failed
+
+## Example Using Response Fields
+
+```yaml
+action:
+  - action: ha_text_ai.ask_question
+    data:
+      instance: sensor.ha_text_ai_gemini
+      question: "Tell me a joke"
+    response_variable: joke_response
+  
+  - condition: template
+    value_template: "{{ joke_response.success }}"
+  
+  - action: input_text.set_value
+    target:
+      entity_id: input_text.last_ai_response
+    data:
+      value: "{{ joke_response.response_text }}"
+  
+  - action: input_number.set_value
+    target:
+      entity_id: input_number.tokens_used
+    data:
+      value: "{{ joke_response.tokens_used }}"
+```
+
+## Error Handling
+
+```yaml
+action:
+  - action: ha_text_ai.ask_question
+    data:
+      instance: sensor.ha_text_ai_gemini
+      question: "Test question"
+    response_variable: ai_result
+  
+  - choose:
+      - conditions:
+          - condition: template
+            value_template: "{{ ai_result.success }}"
+        sequence:
+          - action: notify.mobile_app_phone
+            data:
+              title: "AI Response"
+              message: "{{ ai_result.response_text }}"
+      - conditions:
+          - condition: template
+            value_template: "{{ not ai_result.success }}"
+        sequence:
+          - action: notify.mobile_app_phone
+            data:
+              title: "AI Error"
+              message: "Error: {{ ai_result.error }}"
+```
+
+## Migration from Old Approach
+
+**Old method (without response_variable):**
+```yaml
+# Ask question
+- action: ha_text_ai.ask_question
+  data:
+    instance: sensor.ha_text_ai_gemini
+    question: "Hello!"
+
+# Wait and read response from sensor
+- delay: 00:00:05
+- action: notify.mobile_app_phone
+  data:
+    message: "{{ states('sensor.ha_text_ai_gemini') }}"
+```
+
+**New method (with response_variable):**
+```yaml
+# Ask question and get response immediately
+- action: ha_text_ai.ask_question
+  data:
+    instance: sensor.ha_text_ai_gemini
+    question: "Hello!"
+  response_variable: greeting_response
+
+- action: notify.mobile_app_phone
+  data:
+    message: "{{ greeting_response.response_text }}"
+```
+
+The new approach is more reliable as it doesn't require waiting and reading from the sensor.

custom_components/ha_text_ai/__init__.py

@@ -1,24 +1,32 @@
-"""The HA Text AI integration."""
+"""
+The HA Text AI integration.
+
+@license: PolyForm Noncommercial 1.0.0 (https://polyformproject.org/licenses/noncommercial/1.0.0)
+@author: SMKRV
+@github: https://github.com/smkrv/ha-text-ai
+@source: https://github.com/smkrv/ha-text-ai
+"""
 from __future__ import annotations
 
 import logging
-import os
-import shutil
-from datetime import datetime, timedelta
 from typing import Any, Dict
 
+import asyncio
+
 import voluptuous as vol
-from async_timeout import timeout
 
 from homeassistant.config_entries import ConfigEntry
-from homeassistant.const import CONF_API_KEY, CONF_NAME, Platform
-from homeassistant.core import HomeAssistant, ServiceCall
+from homeassistant.const import CONF_API_KEY, CONF_NAME
+from homeassistant.core import HomeAssistant, ServiceCall, SupportsResponse
 from homeassistant.exceptions import ConfigEntryNotReady, HomeAssistantError
 from homeassistant.helpers import config_validation as cv
 from homeassistant.helpers import aiohttp_client
+from homeassistant.util import dt as dt_util
 
 from .coordinator import HATextAICoordinator
 from .api_client import APIClient
+from .utils import normalize_name, safe_log_data, validate_endpoint
+from .providers import get_default_endpoint, get_default_model, build_auth_headers
 from .const import (
     DOMAIN,
     PLATFORMS,
@@ -27,24 +35,22 @@ from .const import (
     CONF_MAX_TOKENS,
     CONF_API_ENDPOINT,
     CONF_REQUEST_INTERVAL,
+    CONF_API_TIMEOUT,
     CONF_API_PROVIDER,
     CONF_CONTEXT_MESSAGES,
-    API_PROVIDER_OPENAI,
-    API_PROVIDER_ANTHROPIC,
-    DEFAULT_MODEL,
     DEFAULT_TEMPERATURE,
     DEFAULT_MAX_TOKENS,
-    DEFAULT_OPENAI_ENDPOINT,
-    DEFAULT_ANTHROPIC_ENDPOINT,
     DEFAULT_REQUEST_INTERVAL,
+    DEFAULT_API_TIMEOUT,
     DEFAULT_CONTEXT_MESSAGES,
-    API_TIMEOUT,
     SERVICE_ASK_QUESTION,
     SERVICE_CLEAR_HISTORY,
     SERVICE_GET_HISTORY,
     SERVICE_SET_SYSTEM_PROMPT,
     DEFAULT_MAX_HISTORY,
     CONF_MAX_HISTORY_SIZE,
+    CONF_ALLOW_LOCAL_NETWORK,
+    DEFAULT_ALLOW_LOCAL_NETWORK,
 )
 
 _LOGGER = logging.getLogger(__name__)
@@ -53,12 +59,16 @@ CONFIG_SCHEMA = cv.config_entry_only_config_schema(DOMAIN)
 
 SERVICE_SCHEMA_ASK_QUESTION = vol.Schema({
     vol.Required("instance"): cv.string,
-    vol.Required("question"): cv.string,
-    vol.Optional("system_prompt"): cv.string,
+    vol.Required("question"): vol.All(cv.string, vol.Length(min=1, max=100000)),
+    vol.Optional("system_prompt"): vol.All(cv.string, vol.Length(max=50000)),
     vol.Optional("model"): cv.string,
-    vol.Optional("temperature"): cv.positive_float,
+    vol.Optional("temperature"): vol.All(
+        vol.Coerce(float), vol.Range(min=0.0, max=2.0)
+    ),
     vol.Optional("max_tokens"): cv.positive_int,
     vol.Optional("context_messages"): cv.positive_int,
+    vol.Optional("structured_output", default=False): cv.boolean,
+    vol.Optional("json_schema"): vol.All(cv.string, vol.Length(max=50000)),
 })
 
 SERVICE_SCHEMA_SET_SYSTEM_PROMPT = vol.Schema({
@@ -70,48 +80,80 @@ SERVICE_SCHEMA_GET_HISTORY = vol.Schema({
     vol.Required("instance"): cv.string,
     vol.Optional("limit"): cv.positive_int,
     vol.Optional("filter_model"): cv.string,
+    vol.Optional("start_date"): cv.string,
+    vol.Optional("include_metadata"): cv.boolean,
+    vol.Optional("sort_order"): vol.In(["newest", "oldest"]),
 })
 
 def get_coordinator_by_instance(hass: HomeAssistant, instance: str) -> HATextAICoordinator:
-    """Get coordinator by instance name."""
+    """Get coordinator by instance name or normalized name.
+
+    Accepts instance_name, normalized_name, or sensor entity_id.
+    """
     if instance.startswith("sensor."):
         instance = instance.replace("sensor.ha_text_ai_", "", 1)
 
+    normalized_input = normalize_name(instance)
+
     for entry_id, coord in hass.data[DOMAIN].items():
-        if isinstance(coord, HATextAICoordinator) and coord.instance_name.lower() == instance.lower():
+        if not isinstance(coord, HATextAICoordinator):
+            continue
+        if (
+            coord.instance_name.lower() == instance.lower()
+            or coord.normalized_name == normalized_input
+        ):
             return coord
 
     raise HomeAssistantError(f"Instance {instance} not found")
 
 async def async_setup(hass: HomeAssistant, config: Dict[str, Any]) -> bool:
-    """Set up the HA Text AI component."""
+    """Set up the Home Assistant Text AI component."""
+    # Initialize domain data storage
     hass.data.setdefault(DOMAIN, {})
 
-    try:
-        source = os.path.join(os.path.dirname(__file__), 'icons', 'icon.svg')
-        dest_dir = os.path.join(hass.config.path('www'), 'icons')
-        os.makedirs(dest_dir, exist_ok=True)
-        dest = os.path.join(dest_dir, 'icon.png')
-        if not os.path.exists(dest):
-            shutil.copyfile(source, dest)
-    except Exception as ex:
-        _LOGGER.warning("Failed to copy custom icon: %s", str(ex))
-
-    async def async_ask_question(call: ServiceCall) -> None:
-        """Handle ask_question service."""
+    async def async_ask_question(call: ServiceCall) -> dict:
+        """Handle ask_question service with response data."""
         try:
             coordinator = get_coordinator_by_instance(hass, call.data["instance"])
-            await coordinator.async_ask_question(
+            response = await coordinator.async_ask_question(
                 question=call.data["question"],
                 model=call.data.get("model"),
                 temperature=call.data.get("temperature"),
                 max_tokens=call.data.get("max_tokens"),
                 system_prompt=call.data.get("system_prompt"),
                 context_messages=call.data.get("context_messages"),
+                structured_output=call.data.get("structured_output", False),
+                json_schema=call.data.get("json_schema"),
             )
+            
+            # Return structured response data
+            return {
+                "response_text": response.get("content", ""),
+                "tokens_used": response.get("tokens", {}).get("total", 0),
+                "prompt_tokens": response.get("tokens", {}).get("prompt", 0),
+                "completion_tokens": response.get("tokens", {}).get("completion", 0),
+                "model_used": response.get("model", call.data.get("model", coordinator.model)),
+                "instance": call.data["instance"],
+                "question": call.data["question"],
+                "timestamp": response.get("timestamp"),
+                "success": True
+            }
         except Exception as err:
             _LOGGER.error("Error asking question: %s", str(err))
-            raise HomeAssistantError(f"Failed to process question: {str(err)}")
+            # Return error response
+            return {
+                "response_text": "",
+                "tokens_used": 0,
+                "prompt_tokens": 0,
+                "completion_tokens": 0,
+                "model_used": call.data.get("model", ""),
+                "instance": call.data["instance"],
+                "question": call.data["question"],
+                "timestamp": dt_util.utcnow().isoformat(),
+                "success": False,
+                "error": str(err),
+                "error_type": type(err).__name__
+            }
 
     async def async_clear_history(call: ServiceCall) -> None:
         """Handle clear_history service."""
@@ -128,7 +170,10 @@ async def async_setup(hass: HomeAssistant, config: Dict[str, Any]) -> bool:
             coordinator = get_coordinator_by_instance(hass, call.data["instance"])
             return await coordinator.async_get_history(
                 limit=call.data.get("limit"),
-                filter_model=call.data.get("filter_model")
+                filter_model=call.data.get("filter_model"),
+                start_date=call.data.get("start_date"),
+                include_metadata=call.data.get("include_metadata", False),
+                sort_order=call.data.get("sort_order", "newest")
             )
         except Exception as err:
             _LOGGER.error("Error getting history: %s", str(err))
@@ -143,11 +188,13 @@ async def async_setup(hass: HomeAssistant, config: Dict[str, Any]) -> bool:
             _LOGGER.error("Error setting system prompt: %s", str(err))
             raise HomeAssistantError(f"Failed to set system prompt: {str(err)}")
 
+    # Register services
     hass.services.async_register(
         DOMAIN,
         SERVICE_ASK_QUESTION,
         async_ask_question,
-        schema=SERVICE_SCHEMA_ASK_QUESTION
+        schema=SERVICE_SCHEMA_ASK_QUESTION,
+        supports_response=SupportsResponse.OPTIONAL
     )
 
     hass.services.async_register(
@@ -161,7 +208,8 @@ async def async_setup(hass: HomeAssistant, config: Dict[str, Any]) -> bool:
         DOMAIN,
         SERVICE_GET_HISTORY,
         async_get_history,
-        schema=SERVICE_SCHEMA_GET_HISTORY
+        schema=SERVICE_SCHEMA_GET_HISTORY,
+        supports_response=SupportsResponse.OPTIONAL
     )
 
     hass.services.async_register(
@@ -173,20 +221,31 @@ async def async_setup(hass: HomeAssistant, config: Dict[str, Any]) -> bool:
 
     return True
 
-async def async_check_api(session, endpoint: str, headers: dict, provider: str) -> bool:
-    """Check API availability for different providers."""
+async def async_check_api(session, endpoint: str, headers: dict, provider: str, api_timeout: int = DEFAULT_API_TIMEOUT) -> bool:
+    """Check API availability using provider registry configuration."""
     try:
-        if provider == API_PROVIDER_ANTHROPIC:
-            check_url = f"{endpoint}/v1/models"
-        else:  # OpenAI
-            check_url = f"{endpoint}/models"
+        from .providers import get_provider_config
+        provider_config = get_provider_config(provider)
+        check_path = provider_config.get("check_path")
 
-        async with timeout(API_TIMEOUT):
+        if check_path is None:
+            # Provider does not support /models check (e.g. Gemini)
+            auth_header = provider_config["auth_header"]
+            auth_value = headers.get(auth_header, "").replace(provider_config.get("auth_prefix", ""), "")
+            if auth_value:
+                return True
+            _LOGGER.error("API key is missing or empty for %s", provider)
+            return False
+
+        check_url = f"{endpoint}{check_path}"
+
+        async with asyncio.timeout(api_timeout):
             async with session.get(check_url, headers=headers) as response:
-                if response.status in [200, 404]:
+                if response.status == 200:
                     return True
                 elif response.status == 401:
-                    raise ConfigEntryNotReady("Invalid API key")
+                    _LOGGER.error("Invalid API key")
+                    return False
                 elif response.status == 429:
                     _LOGGER.warning("Rate limit exceeded during API check")
                     return False
@@ -199,43 +258,46 @@ async def async_check_api(session, endpoint: str, headers: dict, provider: str)
 
 async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
     """Set up HA Text AI from a config entry."""
-    _LOGGER.debug(f"Setting up HA Text AI entry: {entry.data}")
+    _LOGGER.debug("Setting up HA Text AI entry: %s", safe_log_data(dict(entry.data)))
 
     try:
-        if CONF_API_PROVIDER not in entry.data:
+        # Get provider from data or options (options takes precedence)
+        config = {**entry.data, **entry.options}
+        api_provider = config.get(CONF_API_PROVIDER)
+
+        if not api_provider:
             _LOGGER.error("API provider not specified")
             raise ConfigEntryNotReady("API provider is required")
 
-        # Get configuration
         session = aiohttp_client.async_get_clientsession(hass)
-        api_provider = entry.data.get(CONF_API_PROVIDER)
-        model = entry.data.get(CONF_MODEL, DEFAULT_MODEL)
-        endpoint = entry.data.get(
-            CONF_API_ENDPOINT,
-            DEFAULT_OPENAI_ENDPOINT if api_provider == API_PROVIDER_OPENAI
-            else DEFAULT_ANTHROPIC_ENDPOINT
-        ).rstrip('/')
-        api_key = entry.data[CONF_API_KEY]
+
+        model = config.get(CONF_MODEL, get_default_model(api_provider))
+        raw_endpoint = config.get(CONF_API_ENDPOINT, get_default_endpoint(api_provider))
+        allow_local = config.get(CONF_ALLOW_LOCAL_NETWORK, DEFAULT_ALLOW_LOCAL_NETWORK)
+        if allow_local:
+            _LOGGER.warning(
+                "Local network mode enabled for endpoint %s — "
+                "SSRF protection disabled, API credentials may be sent without TLS",
+                raw_endpoint,
+            )
+        try:
+            endpoint = await validate_endpoint(hass, raw_endpoint, allow_local=allow_local)
+        except ValueError as err:
+            _LOGGER.error("Invalid API endpoint: %s", err)
+            raise ConfigEntryNotReady(f"Invalid API endpoint: {err}") from err
+        # API key can now be updated via options
+        api_key = config.get(CONF_API_KEY, entry.data.get(CONF_API_KEY))
         instance_name = entry.data.get(CONF_NAME, entry.entry_id)
-        request_interval = entry.data.get(CONF_REQUEST_INTERVAL, DEFAULT_REQUEST_INTERVAL)
-        max_tokens = entry.data.get(CONF_MAX_TOKENS, DEFAULT_MAX_TOKENS)
-        temperature = entry.data.get(CONF_TEMPERATURE, DEFAULT_TEMPERATURE)
-        max_history_size = entry.data.get(CONF_MAX_HISTORY_SIZE, DEFAULT_MAX_HISTORY)
-        context_messages = entry.data.get(CONF_CONTEXT_MESSAGES, DEFAULT_CONTEXT_MESSAGES)
-        is_anthropic = api_provider == API_PROVIDER_ANTHROPIC
+        request_interval = config.get(CONF_REQUEST_INTERVAL, DEFAULT_REQUEST_INTERVAL)
+        api_timeout = config.get(CONF_API_TIMEOUT, DEFAULT_API_TIMEOUT)
+        max_tokens = config.get(CONF_MAX_TOKENS, DEFAULT_MAX_TOKENS)
+        temperature = config.get(CONF_TEMPERATURE, DEFAULT_TEMPERATURE)
+        max_history_size = config.get(CONF_MAX_HISTORY_SIZE, DEFAULT_MAX_HISTORY)
+        context_messages = config.get(CONF_CONTEXT_MESSAGES, DEFAULT_CONTEXT_MESSAGES)
 
-        headers = {
-            "Content-Type": "application/json",
-            "Accept": "application/json"
-        }
+        headers = build_auth_headers(api_provider, api_key)
 
-        if is_anthropic:
-            headers["x-api-key"] = api_key
-            headers["anthropic-version"] = "2023-06-01"
-        else:
-            headers["Authorization"] = f"Bearer {api_key}"
-
-        if not await async_check_api(session, endpoint, headers, api_provider):
+        if not await async_check_api(session, endpoint, headers, api_provider, api_timeout):
             raise ConfigEntryNotReady("API connection failed")
 
         _LOGGER.debug("Creating API client for %s with endpoint %s", api_provider, endpoint)
@@ -246,6 +308,8 @@ async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
             headers=headers,
             api_provider=api_provider,
             model=model,
+            api_timeout=api_timeout,
+            api_key=api_key,
         )
 
         coordinator = HATextAICoordinator(
@@ -254,45 +318,61 @@ async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
             model=model,
             update_interval=request_interval,
             instance_name=instance_name,
+            config_entry=entry,
             max_tokens=max_tokens,
             temperature=temperature,
             max_history_size=max_history_size,
             context_messages=context_messages,
-            is_anthropic=is_anthropic,
+            api_timeout=api_timeout,
         )
 
-        _LOGGER.debug(f"Created coordinator for {instance_name}")
+        # Initialize coordinator (directories, history, metrics)
+        await coordinator.async_initialize()
+
+        _LOGGER.debug("Created coordinator for %s", instance_name)
 
         # Store coordinator
         hass.data.setdefault(DOMAIN, {})
         hass.data[DOMAIN][entry.entry_id] = coordinator
 
-        _LOGGER.debug(f"Stored coordinator in hass.data[{DOMAIN}][{entry.entry_id}]")
+        _LOGGER.debug("Stored coordinator in hass.data[%s][%s]", DOMAIN, entry.entry_id)
 
         # Set up platforms
         await hass.config_entries.async_forward_entry_setups(entry, PLATFORMS)
 
-        _LOGGER.debug(f"Setup completed for {instance_name}")
+        # Register update listener for options changes
+        entry.async_on_unload(entry.add_update_listener(async_update_options))
+
+        _LOGGER.debug("Setup completed for %s", instance_name)
 
         return True
 
     except Exception as err:
-        _LOGGER.exception(f"Error setting up HA Text AI: {err}")
+        _LOGGER.exception("Error setting up HA Text AI: %s", err)
         raise
 
+async def async_update_options(hass: HomeAssistant, entry: ConfigEntry) -> None:
+    """Handle options update - reload the config entry."""
+    _LOGGER.info("Options updated for %s, reloading integration", entry.title)
+    await hass.config_entries.async_reload(entry.entry_id)
+
+
 async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
     """Unload a config entry."""
     try:
-        if entry.entry_id in hass.data[DOMAIN]:
-            coordinator = hass.data[DOMAIN][entry.entry_id]
+        unload_ok = await hass.config_entries.async_unload_platforms(entry, PLATFORMS)
+        if unload_ok and entry.entry_id in hass.data[DOMAIN]:
+            coordinator = hass.data[DOMAIN].pop(entry.entry_id)
 
             if hasattr(coordinator.client, 'shutdown'):
                 await coordinator.client.shutdown()
 
             await coordinator.async_shutdown()
-            hass.data[DOMAIN].pop(entry.entry_id)
 
-        return await hass.config_entries.async_unload_platforms(entry, PLATFORMS)
+        if not hass.data.get(DOMAIN):
+            hass.data.pop(DOMAIN, None)
+
+        return unload_ok
 
     except Exception as ex:
         _LOGGER.exception("Error unloading entry: %s", str(ex))

custom_components/ha_text_ai/api_client.py

@@ -1,16 +1,26 @@
-"""API Client for HA Text AI."""
+"""
+API Client for HA Text AI.
+
+@license: PolyForm Noncommercial 1.0.0 (https://polyformproject.org/licenses/noncommercial/1.0.0)
+@author: SMKRV
+@github: https://github.com/smkrv/ha-text-ai
+@source: https://github.com/smkrv/ha-text-ai
+"""
+from __future__ import annotations
+
 import logging
 import asyncio
 from typing import Any, Dict, List, Optional
 from aiohttp import ClientSession, ClientTimeout
-from async_timeout import timeout
 
-from homeassistant.core import HomeAssistant
 from homeassistant.exceptions import HomeAssistantError
 from .const import (
-    API_TIMEOUT,
+    DEFAULT_API_TIMEOUT,
     API_RETRY_COUNT,
     API_PROVIDER_ANTHROPIC,
+    API_PROVIDER_DEEPSEEK,
+    API_PROVIDER_OPENAI,
+    API_PROVIDER_GEMINI,
     MIN_TEMPERATURE,
     MAX_TEMPERATURE,
     MIN_MAX_TOKENS,
@@ -30,6 +40,8 @@ class APIClient:
         headers: Dict[str, str],
         api_provider: str,
         model: str,
+        api_timeout: int = DEFAULT_API_TIMEOUT,
+        api_key: Optional[str] = None,
     ) -> None:
         """Initialize API client."""
         self.session = session
@@ -37,21 +49,41 @@ class APIClient:
         self.headers = headers
         self.api_provider = api_provider
         self.model = model
-        self.timeout = ClientTimeout(total=API_TIMEOUT)
+        self.api_timeout = api_timeout
+        self.timeout = ClientTimeout(total=api_timeout)
+        self._api_key = api_key
+        if self.api_provider == API_PROVIDER_GEMINI and not api_key:
+            raise ValueError("Gemini provider requires api_key parameter")
+        self._closed = False
+
+    async def __aenter__(self):
+        """Async context manager entry."""
+        return self
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        """Async context manager exit."""
+        await self.shutdown()
 
     def _validate_parameters(
         self,
         temperature: float,
         max_tokens: int,
     ) -> None:
-        """Validate API parameters."""
+        """Validate API parameters with enhanced type checking."""
+        # Type validation
+        if not isinstance(temperature, (int, float)):
+            raise TypeError(f"Temperature must be a number, got {type(temperature)}")
+        if not isinstance(max_tokens, int):
+            raise TypeError(f"Max tokens must be an integer, got {type(max_tokens)}")
+            
+        # Range validation
         if not MIN_TEMPERATURE <= temperature <= MAX_TEMPERATURE:
             raise ValueError(
-                f"Temperature must be between {MIN_TEMPERATURE} and {MAX_TEMPERATURE}"
+                f"Temperature must be between {MIN_TEMPERATURE} and {MAX_TEMPERATURE}, got {temperature}"
             )
         if not MIN_MAX_TOKENS <= max_tokens <= MAX_MAX_TOKENS:
             raise ValueError(
-                f"Max tokens must be between {MIN_MAX_TOKENS} and {MAX_MAX_TOKENS}"
+                f"Max tokens must be between {MIN_MAX_TOKENS} and {MAX_MAX_TOKENS}, got {max_tokens}"
             )
 
     async def _make_request(
@@ -59,33 +91,61 @@ class APIClient:
         url: str,
         payload: Dict[str, Any],
     ) -> Dict[str, Any]:
-        """Make API request with retry logic."""
-        _LOGGER.debug(f"API Request: URL={url}, Payload={payload}")
+        """Make API request with retry logic for transient errors only."""
+        safe_payload = {k: v for k, v in payload.items() if k not in ['messages', 'system']}
+        _LOGGER.debug("API Request: URL=%s, Safe payload: %s", url, safe_payload)
+
         for attempt in range(API_RETRY_COUNT):
             try:
-                async with timeout(API_TIMEOUT):
-                    async with self.session.post(
-                        url,
-                        json=payload,
-                        headers=self.headers,
-                        timeout=self.timeout,
-                    ) as response:
-                        _LOGGER.debug(f"Response status: {response.status}")
-                        if response.status != 200:
-                            error_data = await response.json()
-                            _LOGGER.error(f"API error: {error_data}")
-                            raise HomeAssistantError(f"API error: {error_data}")
+                async with self.session.post(
+                    url,
+                    json=payload,
+                    headers=self.headers,
+                    timeout=self.timeout,
+                ) as response:
+                    _LOGGER.debug("Response status: %s", response.status)
+                    if response.status == 200:
                         return await response.json()
+
+                    # Try to get error details
+                    error_data = {}
+                    try:
+                        error_data = await response.json()
+                    except Exception:
+                        error_data = {"raw": await response.text()}
+
+                    # Rate limit — retry with backoff
+                    if response.status == 429:
+                        _LOGGER.warning(
+                            "Rate limit on attempt %d/%d", attempt + 1, API_RETRY_COUNT
+                        )
+                        if attempt < API_RETRY_COUNT - 1:
+                            await asyncio.sleep(2 ** attempt)
+                            continue
+                        raise HomeAssistantError("API rate limit exceeded")
+
+                    # Client/server errors — don't retry
+                    truncated_error = str(error_data)[:512]
+                    _LOGGER.error("API error (status %d): %s", response.status, truncated_error)
+                    raise HomeAssistantError(f"API error: status {response.status}")
+
             except asyncio.TimeoutError:
-                _LOGGER.warning(f"Timeout on attempt {attempt + 1}")
+                _LOGGER.warning("Timeout on attempt %d/%d", attempt + 1, API_RETRY_COUNT)
                 if attempt == API_RETRY_COUNT - 1:
                     raise HomeAssistantError("API request timed out")
-                await asyncio.sleep(1 * (attempt + 1))
+                await asyncio.sleep(2 ** attempt)
+            except HomeAssistantError:
+                raise
             except Exception as e:
-                _LOGGER.warning(f"API request failed on attempt {attempt + 1}: {str(e)}")
+                _LOGGER.warning(
+                    "API request failed on attempt %d/%d: %s",
+                    attempt + 1, API_RETRY_COUNT, type(e).__name__,
+                )
                 if attempt == API_RETRY_COUNT - 1:
                     raise
-                await asyncio.sleep(1 * (attempt + 1))
+                await asyncio.sleep(2 ** attempt)
+
+        raise HomeAssistantError("API request failed after all retries")
 
     async def create(
         self,
@@ -93,6 +153,8 @@ class APIClient:
         messages: List[Dict[str, str]],
         temperature: float,
         max_tokens: int,
+        structured_output: bool = False,
+        json_schema: Optional[str] = None,
     ) -> Dict[str, Any]:
         """Create completion using appropriate API."""
         try:
@@ -100,22 +162,94 @@ class APIClient:
 
             if self.api_provider == API_PROVIDER_ANTHROPIC:
                 return await self._create_anthropic_completion(
-                    model, messages, temperature, max_tokens
+                    model, messages, temperature, max_tokens,
+                    structured_output, json_schema
+                )
+            elif self.api_provider == API_PROVIDER_DEEPSEEK:
+                return await self._create_deepseek_completion(
+                    model, messages, temperature, max_tokens,
+                    structured_output, json_schema
+                )
+            elif self.api_provider == API_PROVIDER_GEMINI:
+                return await self._create_gemini_completion(
+                    model, messages, temperature, max_tokens,
+                    structured_output, json_schema
                 )
             else:
                 return await self._create_openai_completion(
-                    model, messages, temperature, max_tokens
+                    model, messages, temperature, max_tokens,
+                    structured_output, json_schema
                 )
         except Exception as e:
             _LOGGER.error("API request failed: %s", str(e))
             raise HomeAssistantError(f"API request failed: {str(e)}")
 
+    @staticmethod
+    def _apply_structured_output(
+        payload: Dict[str, Any],
+        structured_output: bool,
+        json_schema: Optional[str],
+    ) -> None:
+        """Apply OpenAI-compatible structured output to payload in-place."""
+        if not (structured_output and json_schema):
+            return
+        import json
+        try:
+            schema = json.loads(json_schema)
+            payload["response_format"] = {
+                "type": "json_schema",
+                "json_schema": {
+                    "name": "structured_response",
+                    "strict": True,
+                    "schema": schema,
+                },
+            }
+        except json.JSONDecodeError as e:
+            _LOGGER.warning("Invalid JSON schema: %s. Falling back to json_object.", e)
+            payload["response_format"] = {"type": "json_object"}
+
+    async def _create_deepseek_completion(
+        self,
+        model: str,
+        messages: List[Dict[str, str]],
+        temperature: float,
+        max_tokens: int,
+        structured_output: bool = False,
+        json_schema: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """Create completion using DeepSeek API."""
+        url = f"{self.endpoint}/chat/completions"
+        payload = {
+            "model": model,
+            "messages": messages,
+            "temperature": temperature,
+            "max_tokens": max_tokens,
+            "stream": False,
+        }
+        self._apply_structured_output(payload, structured_output, json_schema)
+
+        data = await self._make_request(url, payload)
+        return {
+            "choices": [
+                {
+                    "message": {"content": data["choices"][0]["message"]["content"]},
+                }
+            ],
+            "usage": {
+                "prompt_tokens": data["usage"]["prompt_tokens"],
+                "completion_tokens": data["usage"]["completion_tokens"],
+                "total_tokens": data["usage"]["total_tokens"],
+            },
+        }
+
     async def _create_openai_completion(
         self,
         model: str,
         messages: List[Dict[str, str]],
         temperature: float,
         max_tokens: int,
+        structured_output: bool = False,
+        json_schema: Optional[str] = None,
     ) -> Dict[str, Any]:
         """Create completion using OpenAI API."""
         url = f"{self.endpoint}/chat/completions"
@@ -125,6 +259,7 @@ class APIClient:
             "temperature": temperature,
             "max_tokens": max_tokens,
         }
+        self._apply_structured_output(payload, structured_output, json_schema)
 
         data = await self._make_request(url, payload)
         return {
@@ -146,6 +281,8 @@ class APIClient:
         messages: List[Dict[str, str]],
         temperature: float,
         max_tokens: int,
+        structured_output: bool = False,
+        json_schema: Optional[str] = None,
     ) -> Dict[str, Any]:
         """Create completion using Anthropic API."""
         url = f"{self.endpoint}/v1/messages"
@@ -161,6 +298,20 @@ class APIClient:
             else:
                 filtered_messages.append(msg)
 
+        # For Anthropic, add structured output instruction to system prompt
+        if structured_output and json_schema:
+            schema_instruction = (
+                f"\n\nIMPORTANT: You MUST respond ONLY with valid JSON that matches "
+                f"this JSON Schema:\n{json_schema}\n"
+                f"Do not include any text before or after the JSON. "
+                f"Do not wrap the JSON in markdown code blocks."
+            )
+            if system_prompt:
+                system_prompt += schema_instruction
+            else:
+                system_prompt = schema_instruction.strip()
+            _LOGGER.debug("Anthropic structured output enabled via system prompt")
+
         payload = {
             "model": model,
             "messages": filtered_messages,
@@ -185,16 +336,179 @@ class APIClient:
             },
         }
 
-    async def check_connection(self) -> bool:
-        """Check API connection."""
+    async def _create_gemini_completion(
+        self,
+        model: str,
+        messages: List[Dict[str, str]],
+        temperature: float,
+        max_tokens: int,
+        structured_output: bool = False,
+        json_schema: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """Create completion using Gemini API with google-genai library.
+
+        Args:
+            model: The model name to use
+            messages: List of message dictionaries with role and content
+            temperature: Sampling temperature between 0.0 and 2.0
+            max_tokens: Maximum number of tokens to generate
+            structured_output: Enable JSON structured output mode
+            json_schema: JSON Schema for structured output validation
+
+        Returns:
+            Dictionary with response content and token usage
+        """
         try:
-            await self._make_request(self.endpoint, {"test": "connection"})
-            return True
+            def import_genai():
+                from google import genai
+                return genai
+
+            genai = await asyncio.to_thread(import_genai)
+
+            api_key = self._api_key
+
+            def create_client():
+                if self.endpoint and self.endpoint != "https://generativelanguage.googleapis.com/v1beta":
+                    return genai.Client(api_key=api_key, transport="rest",
+                                       client_options={"api_endpoint": self.endpoint})
+                else:
+                    return genai.Client(api_key=api_key)
+
+            client = await asyncio.to_thread(create_client)
+
+            # Process messages to extract system instruction and chat history
+            system_instruction = ""
+            contents = []
+
+            for msg in messages:
+                if msg['role'] == 'system':
+                    system_instruction += msg['content'] + "\n"
+                else:
+                    # For chat history, we need to convert to the format Gemini expects
+                    role = "user" if msg['role'] == 'user' else "model"
+                    contents.append({
+                        "role": role,
+                        "parts": [{"text": msg['content']}]
+                    })
+
+            # Parse JSON schema if structured output is enabled
+            parsed_schema = None
+            if structured_output and json_schema:
+                try:
+                    import json
+                    parsed_schema = json.loads(json_schema)
+                    _LOGGER.debug("Gemini structured output enabled with schema")
+                except json.JSONDecodeError as e:
+                    _LOGGER.warning("Invalid JSON schema provided: %s. Structured output disabled.", e)
+
+            # Create configuration
+            def create_config():
+                from google.genai import types
+                config = types.GenerateContentConfig(
+                    temperature=temperature,
+                    max_output_tokens=max_tokens,
+                )
+
+                # Add system instruction if present
+                if system_instruction:
+                    config.system_instruction = system_instruction.strip()
+
+                # Add structured output configuration for Gemini
+                if structured_output and parsed_schema:
+                    config.response_mime_type = "application/json"
+                    config.response_schema = parsed_schema
+
+                return config
+
+            config = await asyncio.to_thread(create_config)
+
+            def generate_content():
+                # For single message without history, use generate_content
+                if len(contents) <= 1:
+                    if not contents:
+                        prompt = "I need your assistance."
+                    else:
+                        prompt = contents[0]["parts"][0]["text"]
+
+                    return client.models.generate_content(
+                        model=model,
+                        contents=prompt,
+                        config=config
+                    )
+                else:
+                    # For multi-turn conversations, pass history to chat
+                    # and only send the last user message
+                    last_user_msg = None
+                    history = []
+
+                    # Find the last user message — that's the new query
+                    for i in range(len(contents) - 1, -1, -1):
+                        if contents[i]["role"] == "user":
+                            last_user_msg = contents[i]["parts"][0]["text"]
+                            history = contents[:i]
+                            break
+
+                    if last_user_msg is None:
+                        # No user messages at all — shouldn't happen, but handle gracefully
+                        return client.models.generate_content(
+                            model=model,
+                            contents="I need your assistance.",
+                            config=config
+                        )
+
+                    chat = client.chats.create(
+                        model=model, config=config, history=history
+                    )
+                    return chat.send_message(last_user_msg)
+
+            # Gemini uses sync SDK via to_thread, so needs its own timeout
+            # (aiohttp ClientTimeout doesn't apply here)
+            async with asyncio.timeout(self.api_timeout):
+                response = await asyncio.to_thread(generate_content)
+
+            # Extract response text
+            def extract_response():
+                response_text = response.text if hasattr(response, 'text') else ""
+
+                # Try to get token usage if available
+                usage = {}
+                if hasattr(response, 'usage_metadata'):
+                    usage = {
+                        "prompt_tokens": getattr(response.usage_metadata, 'prompt_token_count', 0),
+                        "completion_tokens": getattr(response.usage_metadata, 'candidates_token_count', 0),
+                        "total_tokens": getattr(response.usage_metadata, 'total_token_count', 0)
+                    }
+                else:
+                    # Estimate token count as fallback
+                    usage = {
+                        "prompt_tokens": len(" ".join([m["content"] for m in messages]).split()) // 3,
+                        "completion_tokens": len(response_text.split()) // 3,
+                        "total_tokens": 0  # Will be calculated below
+                    }
+                    usage["total_tokens"] = usage["prompt_tokens"] + usage["completion_tokens"]
+
+                return response_text, usage
+
+            response_text, usage = await asyncio.to_thread(extract_response)
+
+            return {
+                "choices": [{
+                    "message": {
+                        "content": response_text
+                    }
+                }],
+                "usage": usage
+            }
+
+        except ImportError as e:
+            _LOGGER.error("Google Gemini library not installed: %s", e)
+            raise HomeAssistantError("Missing dependency: google-genai. Please install it.")
         except Exception as e:
-            _LOGGER.error(f"Connection check failed: {str(e)}")
-            return False
+            _LOGGER.error("Gemini API error: %s", e)
+            raise HomeAssistantError("Gemini API request failed")
 
     async def shutdown(self) -> None:
         """Shutdown API client."""
         _LOGGER.debug("Shutting down API client")
-        await self.session.close()
+        self._closed = True
+        # Do NOT close the shared Home Assistant session

custom_components/ha_text_ai/config_flow.py

@@ -1,4 +1,13 @@
-"""Config flow for HA text AI integration."""
+"""
+Config flow for HA text AI integration.
+
+@license: PolyForm Noncommercial 1.0.0 (https://polyformproject.org/licenses/noncommercial/1.0.0)
+@author: SMKRV
+@github: https://github.com/smkrv/ha-text-ai
+@source: https://github.com/smkrv/ha-text-ai
+"""
+from __future__ import annotations
+
 import logging
 from typing import Any, Dict, Optional
 
@@ -6,7 +15,7 @@ import voluptuous as vol
 from homeassistant import config_entries
 from homeassistant.const import CONF_API_KEY, CONF_NAME
 from homeassistant.core import callback
-from homeassistant.data_entry_flow import FlowResult
+from homeassistant.config_entries import ConfigFlowResult
 from homeassistant.helpers.aiohttp_client import async_get_clientsession
 from homeassistant.helpers import selector
 
@@ -17,36 +26,73 @@ from .const import (
     CONF_MAX_TOKENS,
     CONF_API_ENDPOINT,
     CONF_REQUEST_INTERVAL,
+    CONF_API_TIMEOUT,
     CONF_API_PROVIDER,
     CONF_CONTEXT_MESSAGES,
     API_PROVIDER_OPENAI,
     API_PROVIDER_ANTHROPIC,
+    API_PROVIDER_DEEPSEEK,
+    API_PROVIDER_GEMINI,
     API_PROVIDERS,
-    DEFAULT_MODEL,
     DEFAULT_TEMPERATURE,
     DEFAULT_MAX_TOKENS,
     DEFAULT_REQUEST_INTERVAL,
-    DEFAULT_OPENAI_ENDPOINT,
-    DEFAULT_ANTHROPIC_ENDPOINT,
+    DEFAULT_API_TIMEOUT,
     DEFAULT_CONTEXT_MESSAGES,
     MIN_TEMPERATURE,
     MAX_TEMPERATURE,
     MIN_MAX_TOKENS,
     MAX_MAX_TOKENS,
     MIN_REQUEST_INTERVAL,
+    MIN_API_TIMEOUT,
+    MAX_API_TIMEOUT,
     DEFAULT_NAME_PREFIX,
+    DEFAULT_INSTANCE_NAME,
     DEFAULT_MAX_HISTORY,
     CONF_MAX_HISTORY_SIZE,
+    MIN_CONTEXT_MESSAGES,
+    MAX_CONTEXT_MESSAGES,
+    MIN_HISTORY_SIZE,
+    MAX_HISTORY_SIZE,
+    CONF_ALLOW_LOCAL_NETWORK,
+    DEFAULT_ALLOW_LOCAL_NETWORK,
 )
+from homeassistant.util import dt as dt_util
+
+from .utils import normalize_name, safe_log_data, validate_endpoint
+from .providers import get_default_endpoint, get_default_model, build_auth_headers
 
 _LOGGER = logging.getLogger(__name__)
 
 
-def normalize_name(name: str) -> str:
-    """Normalize name to conform to HA naming convention using underscores."""
-    normalized = ''.join(c if c.isalnum() or c == '_' else '_' for c in name)
-    normalized = '_'.join(filter(None, normalized.split('_')))
-    return normalized.lower()
+def _build_parameter_schema(data: Dict[str, Any]) -> dict:
+    """Build shared parameter schema fields used by both ConfigFlow and OptionsFlow."""
+    return {
+        vol.Optional(
+            CONF_TEMPERATURE,
+            default=data.get(CONF_TEMPERATURE, DEFAULT_TEMPERATURE),
+        ): vol.All(vol.Coerce(float), vol.Range(min=MIN_TEMPERATURE, max=MAX_TEMPERATURE)),
+        vol.Optional(
+            CONF_MAX_TOKENS,
+            default=data.get(CONF_MAX_TOKENS, DEFAULT_MAX_TOKENS),
+        ): vol.All(vol.Coerce(int), vol.Range(min=MIN_MAX_TOKENS, max=MAX_MAX_TOKENS)),
+        vol.Optional(
+            CONF_REQUEST_INTERVAL,
+            default=data.get(CONF_REQUEST_INTERVAL, DEFAULT_REQUEST_INTERVAL),
+        ): vol.All(vol.Coerce(float), vol.Range(min=MIN_REQUEST_INTERVAL)),
+        vol.Optional(
+            CONF_API_TIMEOUT,
+            default=data.get(CONF_API_TIMEOUT, DEFAULT_API_TIMEOUT),
+        ): vol.All(vol.Coerce(int), vol.Range(min=MIN_API_TIMEOUT, max=MAX_API_TIMEOUT)),
+        vol.Optional(
+            CONF_CONTEXT_MESSAGES,
+            default=data.get(CONF_CONTEXT_MESSAGES, DEFAULT_CONTEXT_MESSAGES),
+        ): vol.All(vol.Coerce(int), vol.Range(min=MIN_CONTEXT_MESSAGES, max=MAX_CONTEXT_MESSAGES)),
+        vol.Optional(
+            CONF_MAX_HISTORY_SIZE,
+            default=data.get(CONF_MAX_HISTORY_SIZE, DEFAULT_MAX_HISTORY),
+        ): vol.All(vol.Coerce(int), vol.Range(min=MIN_HISTORY_SIZE, max=MAX_HISTORY_SIZE)),
+    }
 
 
 class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
@@ -60,7 +106,7 @@ class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
         self._data = {}
         self._provider = None
 
-    async def async_step_user(self, user_input: Optional[Dict[str, Any]] = None) -> FlowResult:
+    async def async_step_user(self, user_input: Optional[Dict[str, Any]] = None) -> ConfigFlowResult:
         """Handle the initial step."""
         if user_input is None:
             return self.async_show_form(
@@ -78,70 +124,61 @@ class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
         self._provider = user_input[CONF_API_PROVIDER]
         return await self.async_step_provider()
 
-    async def async_step_provider(self, user_input: Optional[Dict[str, Any]] = None) -> FlowResult:
+    def _build_provider_schema(
+        self, data: Optional[Dict[str, Any]] = None
+    ) -> vol.Schema:
+        """Build provider configuration schema with optional defaults from data."""
+        defaults = data or {}
+        schema_dict = {
+            vol.Required(CONF_NAME, default=defaults.get(CONF_NAME, DEFAULT_INSTANCE_NAME)): str,
+            vol.Required(CONF_API_KEY): str,
+            vol.Required(CONF_MODEL, default=defaults.get(CONF_MODEL, get_default_model(self._provider))): str,
+            vol.Required(CONF_API_ENDPOINT, default=defaults.get(CONF_API_ENDPOINT, get_default_endpoint(self._provider))): str,
+            vol.Optional(
+                CONF_ALLOW_LOCAL_NETWORK,
+                default=defaults.get(CONF_ALLOW_LOCAL_NETWORK, DEFAULT_ALLOW_LOCAL_NETWORK),
+            ): bool,
+        }
+        schema_dict.update(_build_parameter_schema(defaults))
+        return vol.Schema(schema_dict)
+
+    async def async_step_provider(self, user_input: Optional[Dict[str, Any]] = None) -> ConfigFlowResult:
         """Handle provider configuration step."""
         self._errors = {}
 
         if user_input is None:
-            default_endpoint = (
-                DEFAULT_OPENAI_ENDPOINT if self._provider == API_PROVIDER_OPENAI
-                else DEFAULT_ANTHROPIC_ENDPOINT
-            )
-
             return self.async_show_form(
                 step_id="provider",
-                data_schema=vol.Schema({
-                    vol.Required(CONF_NAME, default="my_assistant"): str,
-                    vol.Required(CONF_API_KEY): str,
-                    vol.Required(CONF_MODEL, default=DEFAULT_MODEL): str,
-                    vol.Required(CONF_API_ENDPOINT, default=default_endpoint): str,
-                    vol.Optional(CONF_TEMPERATURE, default=DEFAULT_TEMPERATURE): vol.All(
-                        vol.Coerce(float),
-                        vol.Range(min=MIN_TEMPERATURE, max=MAX_TEMPERATURE)
-                    ),
-                    vol.Optional(CONF_MAX_TOKENS, default=DEFAULT_MAX_TOKENS): vol.All(
-                        vol.Coerce(int),
-                        vol.Range(min=MIN_MAX_TOKENS, max=MAX_MAX_TOKENS)
-                    ),
-                    vol.Optional(CONF_REQUEST_INTERVAL, default=DEFAULT_REQUEST_INTERVAL): vol.All(
-                        vol.Coerce(float),
-                        vol.Range(min=MIN_REQUEST_INTERVAL)
-                    ),
-                    vol.Optional(
-                        CONF_CONTEXT_MESSAGES,
-                        default=DEFAULT_CONTEXT_MESSAGES
-                    ): vol.All(
-                        vol.Coerce(int),
-                        vol.Range(min=1, max=20)
-                    ),
-                    vol.Optional(
-                        CONF_MAX_HISTORY_SIZE,
-                        default=DEFAULT_MAX_HISTORY
-                    ): vol.All(
-                        vol.Coerce(int),
-                        vol.Range(min=1, max=100)
-                    ),
-                })
+                data_schema=self._build_provider_schema(),
             )
 
+        _LOGGER.debug("Provider step input data: %s", safe_log_data(user_input))
+
         input_copy = user_input.copy()
 
+        # Check if CONF_NAME exists in input_copy and ensure it's not empty
+        if CONF_NAME not in input_copy or not input_copy[CONF_NAME]:
+            _LOGGER.warning("Missing name in configuration input: %s", safe_log_data(input_copy))
+            input_copy[CONF_NAME] = f"assistant_{dt_util.utcnow().strftime('%Y%m%d_%H%M%S')}"
+            _LOGGER.info("Auto-generated name: %s", input_copy[CONF_NAME])
+
+        # Ensure API key is present
+        if CONF_API_KEY not in input_copy or not input_copy[CONF_API_KEY]:
+            self._errors["base"] = "invalid_auth"
+            _LOGGER.error("API validation error: 'api_key'")
+            return self.async_show_form(
+                step_id="provider",
+                data_schema=self._build_provider_schema(input_copy),
+                errors=self._errors
+            )
+
         try:
             normalized_name = self._validate_and_normalize_name(input_copy[CONF_NAME])
             input_copy[CONF_NAME] = normalized_name
         except ValueError as e:
             return self.async_show_form(
                 step_id="provider",
-                data_schema=vol.Schema({
-                    vol.Required(CONF_NAME, default=input_copy[CONF_NAME]): str,
-                    vol.Required(CONF_API_KEY, default=input_copy[CONF_API_KEY]): str,
-                    vol.Required(CONF_MODEL, default=input_copy[CONF_MODEL]): str,
-                    vol.Required(CONF_API_ENDPOINT, default=input_copy[CONF_API_ENDPOINT]): str,
-                    vol.Optional(CONF_TEMPERATURE, default=input_copy.get(CONF_TEMPERATURE, DEFAULT_TEMPERATURE)): vol.All(
-                        vol.Coerce(float),
-                        vol.Range(min=MIN_TEMPERATURE, max=MAX_TEMPERATURE)
-                    ),
-                }),
+                data_schema=self._build_provider_schema(input_copy),
                 errors={"name": str(e)}
             )
 
@@ -149,69 +186,77 @@ class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
             if not await self._async_validate_api(input_copy):
                 return self.async_show_form(
                     step_id="provider",
-                    data_schema=vol.Schema({
-                    }),
+                    data_schema=self._build_provider_schema(input_copy),
                     errors=self._errors
                 )
-        except Exception as e:
+        except Exception:
+            _LOGGER.exception("Unexpected error during API validation")
             return self.async_show_form(
                 step_id="provider",
-                data_schema=vol.Schema({
-                }),
-                errors={"base": str(e)}
+                data_schema=self._build_provider_schema(input_copy),
+                errors={"base": "unknown"}
             )
 
         return await self._create_entry(input_copy)
 
     def _validate_and_normalize_name(self, name: str) -> str:
-        """
-        Validate and normalize name with detailed error handling.
+        """Validate and normalize name.
+
+        Truncates before uniqueness check to prevent collisions.
 
         Raises:
-            ValueError: If name is invalid
-
-        Returns:
-            Normalized name
+            ValueError: If name is invalid or already exists.
         """
-        if not name:
+        if not name or not name.strip():
             raise ValueError("empty")
 
-        name = name.strip()
-        normalized = ''.join(
-            c if c.isalnum() or c in ' _' else '_'  # Only allow underscores
-            for c in name
-        )
+        normalized = normalize_name(name.strip())[:50]
 
-        normalized = normalized.replace(' ', '_').lower()
+        if not normalized:
+            raise ValueError("empty")
 
         for entry in self._async_current_entries():
             if entry.data.get(CONF_NAME, "") == normalized:
                 raise ValueError("name_exists")
 
-        normalized = normalized[:50]
-
-        if not normalized:
-            raise ValueError("empty")
-
         return normalized
 
     async def _async_validate_api(self, user_input: Dict[str, Any]) -> bool:
-        """Validate API connection."""
+        """Validate API connection using provider registry."""
         try:
-            session = async_get_clientsession(self.hass)
-            headers = self._get_api_headers(user_input)
-            endpoint = user_input[CONF_API_ENDPOINT].rstrip('/')
+            if CONF_API_KEY not in user_input:
+                _LOGGER.error("API validation error: 'api_key'")
+                self._errors["base"] = "invalid_auth"
+                return False
 
-            check_url = (
-                f"{endpoint}/v1/models" if self._provider == API_PROVIDER_ANTHROPIC
-                else f"{endpoint}/models"
-            )
+            try:
+                allow_local = user_input.get(CONF_ALLOW_LOCAL_NETWORK, DEFAULT_ALLOW_LOCAL_NETWORK)
+                endpoint = await validate_endpoint(
+                    self.hass, user_input[CONF_API_ENDPOINT], allow_local=allow_local
+                )
+            except ValueError as err:
+                _LOGGER.error("Endpoint validation failed: %s", err)
+                self._errors["base"] = "cannot_connect"
+                return False
+
+            if self._provider == API_PROVIDER_GEMINI:
+                if not user_input[CONF_API_KEY]:
+                    self._errors["base"] = "invalid_auth"
+                    return False
+                return True
+
+            session = async_get_clientsession(self.hass)
+            headers = build_auth_headers(self._provider, user_input[CONF_API_KEY])
+
+            from .providers import get_provider_config
+            check_path = get_provider_config(self._provider).get("check_path", "/models")
+            check_url = f"{endpoint}{check_path}"
 
             async with session.get(check_url, headers=headers) as response:
                 if response.status == 401:
                     self._errors["base"] = "invalid_auth"
                     return False
-                elif response.status not in [200, 404]:
+                elif response.status != 200:
                     self._errors["base"] = "cannot_connect"
                     return False
                 return True
@@ -221,48 +266,33 @@ class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
             self._errors["base"] = "cannot_connect"
             return False
 
-    def _get_api_headers(self, user_input: Dict[str, Any]) -> Dict[str, str]:
-        """Get API headers based on provider."""
-        api_key = user_input[CONF_API_KEY]
-
-        if self._provider == API_PROVIDER_ANTHROPIC:
-            return {
-                "x-api-key": api_key,
-                "anthropic-version": "2023-06-01",
-                "Content-Type": "application/json"
-            }
-        return {
-            "Authorization": f"Bearer {api_key}",
-            "Content-Type": "application/json"
-        }
-
-    async def _create_entry(self, user_input: Dict[str, Any]) -> FlowResult:
-        """Create the config entry with comprehensive data preservation."""
+    async def _create_entry(self, user_input: Dict[str, Any]) -> ConfigFlowResult:
+        """Create the config entry with unique_id deduplication."""
         instance_name = user_input[CONF_NAME]
         normalized_name = normalize_name(instance_name)
 
-        unique_id = f"{DOMAIN}_{normalized_name}_{self._provider}".lower()
+        unique_id = f"{DOMAIN}_{normalized_name}_{self._provider}"
+        await self.async_set_unique_id(unique_id)
+        self._abort_if_unique_id_configured()
+
+        default_model = get_default_model(self._provider)
 
         entry_data = {
             CONF_API_PROVIDER: self._provider,
             CONF_NAME: instance_name,
-            "normalized_name": normalized_name,
             CONF_API_KEY: user_input.get(CONF_API_KEY),
             CONF_API_ENDPOINT: user_input.get(CONF_API_ENDPOINT),
-            "unique_id": unique_id,
-            CONF_MODEL: user_input.get(CONF_MODEL, DEFAULT_MODEL),
+            CONF_MODEL: user_input.get(CONF_MODEL, default_model),
             CONF_TEMPERATURE: user_input.get(CONF_TEMPERATURE, DEFAULT_TEMPERATURE),
             CONF_MAX_TOKENS: user_input.get(CONF_MAX_TOKENS, DEFAULT_MAX_TOKENS),
             CONF_REQUEST_INTERVAL: user_input.get(CONF_REQUEST_INTERVAL, DEFAULT_REQUEST_INTERVAL),
+            CONF_API_TIMEOUT: user_input.get(CONF_API_TIMEOUT, DEFAULT_API_TIMEOUT),
             CONF_CONTEXT_MESSAGES: user_input.get(CONF_CONTEXT_MESSAGES, DEFAULT_CONTEXT_MESSAGES),
             CONF_MAX_HISTORY_SIZE: user_input.get(CONF_MAX_HISTORY_SIZE, DEFAULT_MAX_HISTORY),
+            CONF_ALLOW_LOCAL_NETWORK: user_input.get(CONF_ALLOW_LOCAL_NETWORK, DEFAULT_ALLOW_LOCAL_NETWORK),
         }
 
-        for key, value in user_input.items():
-            if key not in entry_data:
-                entry_data[key] = value
-
-        _LOGGER.debug(f"Creating config entry with data: {entry_data}")
+        _LOGGER.debug("Creating config entry with data: %s", safe_log_data(entry_data))
 
         return self.async_create_entry(
             title=instance_name,
@@ -273,70 +303,189 @@ class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
     @callback
     def async_get_options_flow(config_entry: config_entries.ConfigEntry) -> config_entries.OptionsFlow:
         """Get the options flow for this handler."""
-        return OptionsFlowHandler(config_entry)
+        return OptionsFlowHandler()
 
 
 class OptionsFlowHandler(config_entries.OptionsFlow):
     """Handle options flow."""
 
-    def __init__(self, config_entry: config_entries.ConfigEntry) -> None:
-        """Initialize options flow."""
-        self.config_entry = config_entry
+    async def _async_validate_api(self, provider: str, api_key: str, endpoint: str, *, allow_local: bool = False) -> bool:
+        """Validate API connection using provider registry."""
+        try:
+            if not api_key:
+                self._errors["base"] = "invalid_auth"
+                return False
 
-    async def async_step_init(self, user_input: Optional[Dict[str, Any]] = None) -> FlowResult:
-        """Manage the options."""
-        if user_input is not None:
-            return self.async_create_entry(title="", data=user_input)
+            try:
+                endpoint = await validate_endpoint(self.hass, endpoint, allow_local=allow_local)
+            except ValueError as err:
+                _LOGGER.error("Endpoint validation failed: %s", err)
+                self._errors["base"] = "cannot_connect"
+                return False
 
+            if provider == API_PROVIDER_GEMINI:
+                return True
+
+            session = async_get_clientsession(self.hass)
+            headers = build_auth_headers(provider, api_key)
+
+            from .providers import get_provider_config
+            check_path = get_provider_config(provider).get("check_path", "/models")
+            check_url = f"{endpoint}{check_path}"
+
+            async with session.get(check_url, headers=headers) as response:
+                if response.status == 401:
+                    self._errors["base"] = "invalid_auth"
+                    return False
+                elif response.status != 200:
+                    self._errors["base"] = "cannot_connect"
+                    return False
+                return True
+
+        except Exception as err:
+            _LOGGER.error("API validation error: %s", str(err))
+            self._errors["base"] = "cannot_connect"
+            return False
+
+    async def async_step_init(self, user_input: Optional[Dict[str, Any]] = None) -> ConfigFlowResult:
+        """Handle provider selection step."""
+        if not hasattr(self, "_errors"):
+            self._errors: dict[str, str] = {}
+            self._selected_provider: Optional[str] = None
         current_data = {**self.config_entry.data, **self.config_entry.options}
+        current_provider = current_data.get(CONF_API_PROVIDER, API_PROVIDER_OPENAI)
+
+        if user_input is not None:
+            self._selected_provider = user_input.get(CONF_API_PROVIDER, current_provider)
+            return await self.async_step_settings()
 
         return self.async_show_form(
             step_id="init",
             data_schema=vol.Schema({
-                vol.Optional(
-                    CONF_MODEL,
-                    default=current_data.get(CONF_MODEL, DEFAULT_MODEL)
-                ): str,
-                vol.Optional(
-                    CONF_TEMPERATURE,
-                    default=current_data.get(CONF_TEMPERATURE, DEFAULT_TEMPERATURE)
-                ): vol.All(
-                    vol.Coerce(float),
-                    vol.Range(min=MIN_TEMPERATURE, max=MAX_TEMPERATURE)
-                ),
-                vol.Optional(
-                    CONF_MAX_TOKENS,
-                    default=current_data.get(CONF_MAX_TOKENS, DEFAULT_MAX_TOKENS)
-                ): vol.All(
-                    vol.Coerce(int),
-                    vol.Range(min=MIN_MAX_TOKENS, max=MAX_MAX_TOKENS)
-                ),
-                vol.Optional(
-                    CONF_REQUEST_INTERVAL,
-                    default=current_data.get(CONF_REQUEST_INTERVAL, DEFAULT_REQUEST_INTERVAL)
-                ): vol.All(
-                    vol.Coerce(float),
-                    vol.Range(min=MIN_REQUEST_INTERVAL)
-                ),
-                vol.Optional(
-                    CONF_CONTEXT_MESSAGES,
-                    default=current_data.get(
-                        CONF_CONTEXT_MESSAGES,
-                        DEFAULT_CONTEXT_MESSAGES
+                vol.Required(
+                    CONF_API_PROVIDER,
+                    default=current_provider
+                ): selector.SelectSelector(
+                    selector.SelectSelectorConfig(
+                        options=API_PROVIDERS,
+                        translation_key="api_provider"
                     )
-                ): vol.All(
-                    vol.Coerce(int),
-                    vol.Range(min=1, max=20)
                 ),
-                vol.Optional(
-                    CONF_MAX_HISTORY_SIZE,
-                    default=current_data.get(
-                        CONF_MAX_HISTORY_SIZE,
-                        DEFAULT_MAX_HISTORY
-                    )
-                ): vol.All(
-                    vol.Coerce(int),
-                    vol.Range(min=1, max=100)
-                ),
-            })
+            }),
+            description_placeholders={
+                "current_provider": current_provider
+            }
         )
+
+    async def async_step_settings(self, user_input: Optional[Dict[str, Any]] = None) -> ConfigFlowResult:
+        """Handle settings configuration step."""
+        self._errors = {}
+        current_data = {**self.config_entry.data, **self.config_entry.options}
+        provider = self._selected_provider or current_data.get(CONF_API_PROVIDER, API_PROVIDER_OPENAI)
+
+        # Determine if provider changed to show appropriate defaults
+        provider_changed = provider != current_data.get(CONF_API_PROVIDER)
+
+        # Use new defaults if provider changed, otherwise use current values
+        if provider_changed:
+            default_endpoint = get_default_endpoint(provider)
+            default_model = get_default_model(provider)
+        else:
+            default_endpoint = current_data.get(CONF_API_ENDPOINT, get_default_endpoint(provider))
+            default_model = current_data.get(CONF_MODEL, get_default_model(provider))
+
+        if user_input is not None:
+            api_key = user_input.get(CONF_API_KEY, "").strip()
+            endpoint = user_input.get(CONF_API_ENDPOINT, default_endpoint)
+
+            # Require API key re-entry when endpoint or provider changed
+            stored_endpoint = current_data.get(CONF_API_ENDPOINT, "")
+            endpoint_changed = endpoint != stored_endpoint
+            if not api_key and (provider_changed or endpoint_changed):
+                self._errors["base"] = "api_key_required"
+                return self.async_show_form(
+                    step_id="settings",
+                    data_schema=self._get_settings_schema(
+                        provider=provider,
+                        current_data=current_data,
+                        user_input=user_input,
+                        default_endpoint=default_endpoint,
+                        default_model=default_model,
+                    ),
+                    errors=self._errors,
+                    description_placeholders={
+                        "provider": provider
+                    }
+                )
+
+            # Fall back to stored key if not re-entered and endpoint unchanged
+            if not api_key:
+                api_key = current_data.get(CONF_API_KEY, "")
+
+            allow_local = user_input.get(CONF_ALLOW_LOCAL_NETWORK, DEFAULT_ALLOW_LOCAL_NETWORK)
+            if await self._async_validate_api(provider, api_key, endpoint, allow_local=allow_local):
+                final_data = {
+                    CONF_API_PROVIDER: provider,
+                    **user_input,
+                    CONF_API_KEY: api_key,
+                }
+                return self.async_create_entry(title="", data=final_data)
+
+            # Show form again with errors
+            return self.async_show_form(
+                step_id="settings",
+                data_schema=self._get_settings_schema(
+                    provider=provider,
+                    current_data=current_data,
+                    user_input=user_input,
+                    default_endpoint=default_endpoint,
+                    default_model=default_model,
+                ),
+                errors=self._errors,
+                description_placeholders={
+                    "provider": provider
+                }
+            )
+
+        return self.async_show_form(
+            step_id="settings",
+            data_schema=self._get_settings_schema(
+                provider=provider,
+                current_data=current_data,
+                user_input=None,
+                default_endpoint=default_endpoint,
+                default_model=default_model,
+            ),
+            description_placeholders={
+                "provider": provider
+            }
+        )
+
+    def _get_settings_schema(
+        self,
+        provider: str,
+        current_data: Dict[str, Any],
+        user_input: Optional[Dict[str, Any]],
+        default_endpoint: str,
+        default_model: str,
+    ) -> vol.Schema:
+        """Build settings schema using shared parameter definitions."""
+        data = user_input or current_data
+
+        schema_dict = {
+            vol.Optional(CONF_API_KEY, default=""): str,
+            vol.Required(
+                CONF_API_ENDPOINT,
+                default=data.get(CONF_API_ENDPOINT, default_endpoint),
+            ): str,
+            vol.Required(
+                CONF_MODEL,
+                default=data.get(CONF_MODEL, default_model),
+            ): str,
+            vol.Optional(
+                CONF_ALLOW_LOCAL_NETWORK,
+                default=data.get(CONF_ALLOW_LOCAL_NETWORK, DEFAULT_ALLOW_LOCAL_NETWORK),
+            ): bool,
+        }
+        schema_dict.update(_build_parameter_schema(data))
+        return vol.Schema(schema_dict)

custom_components/ha_text_ai/const.py

@@ -1,26 +1,41 @@
-"""Constants for the HA text AI integration."""
+"""
+Constants for the HA text AI integration.
+
+@license: PolyForm Noncommercial 1.0.0 (https://polyformproject.org/licenses/noncommercial/1.0.0)
+@author: SMKRV
+@github: https://github.com/smkrv/ha-text-ai
+@source: https://github.com/smkrv/ha-text-ai
+"""
+from __future__ import annotations
+
 from typing import Final
-import voluptuous as vol
-from homeassistant.const import Platform, CONF_API_KEY, CONF_NAME
-from homeassistant.helpers import config_validation as cv
+from homeassistant.const import Platform
 
 # Domain and platforms
 DOMAIN: Final = "ha_text_ai"
-PLATFORMS: list[str] = ["sensor"]
+PLATFORMS: list[Platform] = [Platform.SENSOR]
 
 # Provider configuration
 CONF_API_PROVIDER: Final = "api_provider"
 API_PROVIDER_OPENAI: Final = "openai"
 API_PROVIDER_ANTHROPIC: Final = "anthropic"
+API_PROVIDER_DEEPSEEK: Final = "deepseek"
+API_PROVIDER_GEMINI: Final = "gemini"
 
 API_PROVIDERS: Final = [
     API_PROVIDER_OPENAI,
-    API_PROVIDER_ANTHROPIC
+    API_PROVIDER_ANTHROPIC,
+    API_PROVIDER_DEEPSEEK,
+    API_PROVIDER_GEMINI
 ]
 
+VERSION: Final = "2.4.1"
+
 # Default endpoints
 DEFAULT_OPENAI_ENDPOINT: Final = "https://api.openai.com/v1"
 DEFAULT_ANTHROPIC_ENDPOINT: Final = "https://api.anthropic.com"
+DEFAULT_DEEPSEEK_ENDPOINT: Final = "https://api.deepseek.com"
+DEFAULT_GEMINI_ENDPOINT: Final = "https://generativelanguage.googleapis.com/v1beta"
 
 # Configuration constants
 CONF_MODEL: Final = "model"
@@ -28,32 +43,50 @@ CONF_TEMPERATURE: Final = "temperature"
 CONF_MAX_TOKENS: Final = "max_tokens"
 CONF_API_ENDPOINT: Final = "api_endpoint"
 CONF_REQUEST_INTERVAL: Final = "request_interval"
+CONF_API_TIMEOUT: Final = "api_timeout"
 CONF_INSTANCE: Final = "instance"
 CONF_MAX_HISTORY_SIZE: Final = "max_history_size"  # Correct constant name
-CONF_IS_ANTHROPIC: Final = "is_anthropic"
 CONF_CONTEXT_MESSAGES: Final = "context_messages"
+CONF_STRUCTURED_OUTPUT: Final = "structured_output"
+CONF_JSON_SCHEMA: Final = "json_schema"
+CONF_ALLOW_LOCAL_NETWORK: Final = "allow_local_network"
 
+ABSOLUTE_MAX_HISTORY_SIZE: Final = 200  # Hard cap; UI allows max MAX_HISTORY_SIZE (100)
+MAX_ATTRIBUTE_SIZE = 4 * 1024
+MAX_HISTORY_FILE_SIZE = 1 * 1024 * 1024
 # Default values
 DEFAULT_MODEL: Final = "gpt-4o-mini"
+DEFAULT_ANTHROPIC_MODEL: Final = "claude-sonnet-4-6"
+DEFAULT_DEEPSEEK_MODEL: Final = "deepseek-chat"
+DEFAULT_GEMINI_MODEL: Final = "gemini-2.0-flash"
 DEFAULT_TEMPERATURE: Final = 0.1
 DEFAULT_MAX_TOKENS: Final = 1000
 DEFAULT_REQUEST_INTERVAL: Final = 1.0
-DEFAULT_TIMEOUT: Final = 30
+DEFAULT_API_TIMEOUT: Final = 30
 DEFAULT_MAX_HISTORY: Final = 50
 DEFAULT_NAME: Final = "HA Text AI"
 DEFAULT_NAME_PREFIX = "ha_text_ai"
+DEFAULT_INSTANCE_NAME: Final = "my_assistant"
 DEFAULT_CONTEXT_MESSAGES: Final = 5
+DEFAULT_ALLOW_LOCAL_NETWORK: Final = False
+MIN_CONTEXT_MESSAGES: Final = 1
+MAX_CONTEXT_MESSAGES: Final = 20
+MIN_HISTORY_SIZE: Final = 1
+MAX_HISTORY_SIZE: Final = 100
+
+TRUNCATION_INDICATOR = " ... "
 
 # Parameter constraints
 MIN_TEMPERATURE: Final = 0.0
 MAX_TEMPERATURE: Final = 2.0
 MIN_MAX_TOKENS: Final = 1
-MAX_MAX_TOKENS: Final = 4096
+MAX_MAX_TOKENS: Final = 100000
 MIN_REQUEST_INTERVAL: Final = 0.1
 MAX_REQUEST_INTERVAL: Final = 60.0
+MIN_API_TIMEOUT: Final = 5
+MAX_API_TIMEOUT: Final = 600
 
 # API constants
-API_TIMEOUT: Final = 30
 API_RETRY_COUNT: Final = 3
 
 # Service names
@@ -135,68 +168,3 @@ STATE_DISCONNECTED: Final = "disconnected"
 EVENT_RESPONSE_RECEIVED: Final = f"{DOMAIN}_response_received"
 EVENT_ERROR_OCCURRED: Final = f"{DOMAIN}_error_occurred"
 EVENT_STATE_CHANGED: Final = f"{DOMAIN}_state_changed"
-
-# Service schema constants
-SERVICE_SCHEMA_ASK_QUESTION = vol.Schema({
-    vol.Required(CONF_INSTANCE): cv.string,
-    vol.Required("question"): cv.string,
-    vol.Optional("system_prompt"): cv.string,
-    vol.Optional("model"): cv.string,
-    vol.Optional("temperature"): vol.All(
-        vol.Coerce(float),
-        vol.Range(min=MIN_TEMPERATURE, max=MAX_TEMPERATURE)
-    ),
-    vol.Optional("max_tokens"): vol.All(
-        vol.Coerce(int),
-        vol.Range(min=MIN_MAX_TOKENS, max=MAX_MAX_TOKENS)
-    ),
-    vol.Optional("context_messages"): vol.All(
-        vol.Coerce(int),
-        vol.Range(min=1, max=20)
-    )
-})
-
-SERVICE_SCHEMA_SET_SYSTEM_PROMPT = vol.Schema({
-    vol.Required(CONF_INSTANCE): cv.string,
-    vol.Required("prompt"): cv.string
-})
-
-SERVICE_SCHEMA_GET_HISTORY = vol.Schema({
-    vol.Required(CONF_INSTANCE): cv.string,
-    vol.Optional("limit", default=10): vol.All(
-        vol.Coerce(int),
-        vol.Range(min=1, max=100),
-    ),
-    vol.Optional("filter_model"): cv.string
-})
-
-# Configuration schema
-CONFIG_SCHEMA = vol.Schema({
-    DOMAIN: vol.Schema({
-        vol.Required(CONF_NAME): cv.string,
-        vol.Required(CONF_API_KEY): cv.string,
-        vol.Required(CONF_API_PROVIDER): vol.In(API_PROVIDERS),
-        vol.Optional(CONF_MODEL, default=DEFAULT_MODEL): cv.string,
-        vol.Optional(CONF_TEMPERATURE, default=DEFAULT_TEMPERATURE): vol.All(
-            vol.Coerce(float),
-            vol.Range(min=MIN_TEMPERATURE, max=MAX_TEMPERATURE)
-        ),
-        vol.Optional(CONF_MAX_TOKENS, default=DEFAULT_MAX_TOKENS): vol.All(
-            vol.Coerce(int),
-            vol.Range(min=MIN_MAX_TOKENS, max=MAX_MAX_TOKENS)
-        ),
-        vol.Optional(CONF_API_ENDPOINT): cv.string,
-        vol.Optional(CONF_REQUEST_INTERVAL, default=DEFAULT_REQUEST_INTERVAL): vol.All(
-            vol.Coerce(float),
-            vol.Range(min=MIN_REQUEST_INTERVAL, max=MAX_REQUEST_INTERVAL)
-        ),
-        vol.Optional(CONF_MAX_HISTORY_SIZE, default=DEFAULT_MAX_HISTORY): vol.All(  # Correct usage
-            vol.Coerce(int),
-            vol.Range(min=1, max=100),
-        ),
-        vol.Optional(CONF_CONTEXT_MESSAGES, default=DEFAULT_CONTEXT_MESSAGES): vol.All(
-            vol.Coerce(int),
-            vol.Range(min=1, max=20)
-        )
-    })
-}, extra=vol.ALLOW_EXTRA)

custom_components/ha_text_ai/coordinator.py

@@ -1,38 +1,47 @@
-"""The HA Text AI coordinator."""
+"""
+The HA Text AI coordinator.
+
+@license: PolyForm Noncommercial 1.0.0 (https://polyformproject.org/licenses/noncommercial/1.0.0)
+@author: SMKRV
+@github: https://github.com/smkrv/ha-text-ai
+@source: https://github.com/smkrv/ha-text-ai
+"""
 from __future__ import annotations
 
+import asyncio
 import logging
-import traceback
-from datetime import datetime, timedelta
+import os
+from datetime import timedelta
 from typing import Any, Dict, List, Optional
 
+from homeassistant.config_entries import ConfigEntry
 from homeassistant.core import HomeAssistant
+from homeassistant.exceptions import HomeAssistantError
 from homeassistant.helpers.update_coordinator import DataUpdateCoordinator
 from homeassistant.util import dt as dt_util
-from homeassistant.exceptions import HomeAssistantError
-from homeassistant.const import CONF_NAME
-from .config_flow import normalize_name 
 
 from .const import (
-    DOMAIN,
-    STATE_READY,
-    STATE_PROCESSING,
-    STATE_ERROR,
-    STATE_RATE_LIMITED,
-    STATE_MAINTENANCE,
+    DEFAULT_API_TIMEOUT,
+    DEFAULT_CONTEXT_MESSAGES,
+    DEFAULT_MAX_HISTORY,
     DEFAULT_MAX_TOKENS,
     DEFAULT_TEMPERATURE,
-    DEFAULT_MAX_HISTORY,
-    DEFAULT_CONTEXT_MESSAGES,
-    DEFAULT_NAME_PREFIX,
-    CONF_MAX_HISTORY_SIZE,
+    STATE_ERROR,
+    STATE_MAINTENANCE,
+    STATE_PROCESSING,
+    STATE_RATE_LIMITED,
+    STATE_READY,
+    TRUNCATION_INDICATOR,
 )
+from .history import HistoryManager
+from .metrics import MetricsManager
+from .utils import normalize_name
 
 _LOGGER = logging.getLogger(__name__)
 
 
 class HATextAICoordinator(DataUpdateCoordinator):
-    """The HA Text AI coordinator."""
+    """Home Assistant Text AI Conversation Coordinator."""
 
     def __init__(
         self,
@@ -41,214 +50,159 @@ class HATextAICoordinator(DataUpdateCoordinator):
         model: str,
         update_interval: int,
         instance_name: str,
+        config_entry: ConfigEntry,
         max_tokens: int = DEFAULT_MAX_TOKENS,
         temperature: float = DEFAULT_TEMPERATURE,
         max_history_size: int = DEFAULT_MAX_HISTORY,
         context_messages: int = DEFAULT_CONTEXT_MESSAGES,
-        is_anthropic: bool = False,
+        api_timeout: int = DEFAULT_API_TIMEOUT,
     ) -> None:
         """Initialize coordinator."""
         self.instance_name = instance_name
-        self.normalized_name = None
-
-        # Use the normalize_name function from config_flow to ensure consistency
-        from .config_flow import normalize_name
         self.normalized_name = normalize_name(instance_name)
 
+        history_dir = os.path.join(
+            hass.config.path(".storage"), "ha_text_ai_history"
+        )
+        metrics_file = os.path.join(
+            history_dir,
+            f"ha_text_ai_metrics_{self.normalized_name}.json",
+        )
+
+        # Delegate history and metrics to dedicated managers
+        self._history = HistoryManager(
+            hass=hass,
+            instance_name=instance_name,
+            normalized_name=self.normalized_name,
+            history_dir=history_dir,
+            max_history_size=max_history_size,
+        )
+        self._metrics = MetricsManager(
+            hass=hass,
+            instance_name=instance_name,
+            metrics_file=metrics_file,
+        )
+
         self.hass = hass
         self.client = client
         self.model = model
         self.temperature = temperature
         self.max_tokens = max_tokens
-        self.max_history_size = max_history_size
-        self.is_anthropic = is_anthropic
+        self.api_timeout = api_timeout
 
-        # Initialize with default state
-        self._initial_state = {
-            "state": STATE_READY,
-            "metrics": {
-                "total_tokens": 0,
-                "prompt_tokens": 0,
-                "completion_tokens": 0,
-                "successful_requests": 0,
-                "failed_requests": 0,
-                "total_errors": 0,
-                "average_latency": 0,
-                "max_latency": 0,
-                "min_latency": float("inf"),
-            },
-            "last_response": {
-                "timestamp": dt_util.utcnow().isoformat(),
-                "question": "",
-                "response": "",
-                "model": model,
-                "instance": instance_name,
-                "normalized_name": self.normalized_name,
-                "error": None,
-            },
-            "is_processing": False,
-            "is_rate_limited": False,
-            "is_maintenance": False,
-            "endpoint_status": "ready",
-            "uptime": 0,
-            "system_prompt": None,
-            "history_size": 0,
-            "conversation_history": [],
+        # Concurrency control
+        self._request_lock = asyncio.Lock()
+
+        # State flags
+        self._is_processing = False
+        self._is_rate_limited = False
+        self._is_maintenance = False
+        self.endpoint_status = "ready"
+        self._system_prompt: Optional[str] = None
+
+        self._last_response: Dict[str, Any] = {
+            "timestamp": dt_util.utcnow().isoformat(),
+            "question": "",
+            "response": "",
+            "model": model,
+            "instance": instance_name,
+            "normalized_name": self.normalized_name,
+            "error": None,
         }
 
-        update_interval_td = timedelta(seconds=update_interval)
-
         super().__init__(
             hass,
             _LOGGER,
             name=instance_name,
-            update_interval=update_interval_td,
+            update_interval=timedelta(seconds=update_interval),
+            config_entry=config_entry,
         )
 
-        # Register instance
-        self.hass.data.setdefault(DOMAIN, {})
-        self.hass.data[DOMAIN][instance_name] = self
+        self.available = True
+        self._state = STATE_READY
+        self._start_time = dt_util.utcnow()
         self.context_messages = context_messages
 
-        self._system_prompt = None
-        self._conversation_history = []
-        self._performance_metrics = self._initial_state["metrics"].copy()
-        self._is_processing = False
-        self._is_rate_limited = False
-        self._is_maintenance = False
-        self.endpoint_status = "ready"
-        self.last_response = self._initial_state["last_response"].copy()
-        self._start_time = dt_util.utcnow()
+        _LOGGER.info("Initialized HA Text AI coordinator: %s", instance_name)
 
-        _LOGGER.info(
-            f"Initialized HA Text AI coordinator with instance: {instance_name}"
-        )
+    # ------------------------------------------------------------------
+    # Convenience accessors for backward compatibility
+    # ------------------------------------------------------------------
+    @property
+    def _conversation_history(self) -> List[Dict[str, Any]]:
+        return self._history.conversation_history
+
+    @property
+    def max_history_size(self) -> int:
+        return self._history.max_history_size
+
+    # ------------------------------------------------------------------
+    # Lifecycle
+    # ------------------------------------------------------------------
+    async def async_initialize(self) -> None:
+        """Initialize coordinator: directories, history, metrics. Must be awaited."""
+        await self._history.async_initialize()
+        await self._metrics.async_initialize()
+
+    async def async_shutdown(self) -> None:
+        """Shutdown coordinator."""
+        _LOGGER.debug("Shutting down coordinator for %s", self.instance_name)
+
+    # ------------------------------------------------------------------
+    # Last response
+    # ------------------------------------------------------------------
+    @property
+    def last_response(self) -> Dict[str, Any]:
+        """Get the last response."""
+        return self._last_response
+
+    @last_response.setter
+    def last_response(self, value: Dict[str, Any]) -> None:
+        self._last_response = value
+
+    # ------------------------------------------------------------------
+    # HA state update
+    # ------------------------------------------------------------------
+    async def async_update_ha_state(self) -> None:
+        """Update Home Assistant state via coordinator refresh."""
+        try:
+            await self.async_request_refresh()
+        except Exception as err:
+            _LOGGER.error("Error updating HA state for %s: %s", self.instance_name, err)
 
     async def _async_update_data(self) -> Dict[str, Any]:
-        """Update data via library."""
+        """Update coordinator data."""
         try:
             current_state = self._get_current_state()
-            _LOGGER.debug(
-                f"Updating data for {self.instance_name}, current state: {current_state}"
-            )
+            history_data = self._history.get_limited_history()
+            metrics = await self._metrics.get_current_metrics()
 
             data = {
                 "state": current_state,
-                "metrics": self._performance_metrics,
-                "last_response": self.last_response,
+                "metrics": metrics or {},
+                "last_response": self._get_sanitized_last_response(),
                 "is_processing": self._is_processing,
                 "is_rate_limited": self._is_rate_limited,
                 "is_maintenance": self._is_maintenance,
                 "endpoint_status": self.endpoint_status,
-                "uptime": (dt_util.utcnow() - self._start_time).total_seconds(),
-                "system_prompt": self._system_prompt,
-                "history_size": len(self._conversation_history),
-                "conversation_history": self._conversation_history,
+                "uptime": self._calculate_uptime(),
+                "system_prompt": self._get_truncated_system_prompt(),
+                "history_size": self._history.history_size,
+                "conversation_history": history_data["entries"],
+                "history_info": history_data["info"],
                 "normalized_name": self.normalized_name,
             }
 
-            # Validate data
-            if not isinstance(data, dict):
-                raise ValueError("Invalid data format")
-
-            _LOGGER.debug(f"Updated data for {self.instance_name}: {data}")
+            self._validate_update_data(data)
             return data
 
         except Exception as err:
-            _LOGGER.error(f"Error updating data for {self.instance_name}: {err}")
-            return self._initial_state
-
-    async def async_update_ha_state(self) -> None:
-        """Update Home Assistant state."""
-        try:
-            _LOGGER.debug(
-                f"Requesting state update for {self.instance_name} (normalized: {self.normalized_name})"
-            )
-            await self.async_request_refresh()
-
-            # Force update of all entities
-            entity_id_base = f"sensor.ha_text_ai_{self.normalized_name.lower()}"
-            for entity_id in self.hass.states.async_entity_ids():
-                if entity_id.startswith(entity_id_base):
-                    self.hass.states.async_set(entity_id, self._get_current_state())
-
-        except Exception as err:
-            _LOGGER.error(f"Error updating HA state for {self.instance_name}: {err}")
-
-    def _get_current_state(self) -> str:
-        """Get current state based on internal flags."""
-        if self._is_processing:
-            return STATE_PROCESSING
-        elif self._is_rate_limited:
-            return STATE_RATE_LIMITED
-        elif self._is_maintenance:
-            return STATE_MAINTENANCE
-        elif self.last_response.get("error"):
-            return STATE_ERROR
-        return STATE_READY
-
-    def _calculate_context_tokens(self, messages: List[Dict[str, str]], model: str = None) -> int:
-        """
-        Estimate tokens for conversation context.
-
-        Args:
-            messages: List of message dictionaries
-            model: Optional model name for provider-specific estimation
-
-        Returns:
-            Estimated number of tokens
-        """
-        try:
-            # Anthropic specific token counting
-            if self.is_anthropic and hasattr(self.client, 'count_tokens'):
-                return sum(self.client.count_tokens(msg['content']) for msg in messages)
-
-            def estimate_tokens(text: str) -> int:
-                """
-                Flexible token estimation algorithm.
-
-                Heuristics:
-                - Count words
-                - Estimate special characters
-                - Fallback to character-based estimation
-                """
-                # Word-based estimation
-                words = len(text.split())
-
-                # Special character handling
-                special_chars = sum(1 for char in text if not char.isalnum())
-
-                # Character-based fallback
-                char_tokens = len(text) // 4
-
-                # Combine estimations with bias towards words
-                total_tokens = (words * 1.5) + (special_chars * 0.5) + char_tokens
-
-                return max(int(total_tokens), words)
-
-            # Calculate total tokens across all messages
-            total_tokens = sum(estimate_tokens(msg['content']) for msg in messages)
-
-            # Logging for debugging
-            _LOGGER.debug(
-                f"Token Estimation: "
-                f"Messages: {len(messages)}, "
-                f"Estimated Tokens: {total_tokens}"
-            )
-
-            return total_tokens
-
-        except Exception as e:
-            # Safe fallback with detailed logging
-            _LOGGER.warning(
-                f"Token estimation failed. "
-                f"Error: {e}. "
-                f"Using conservative estimation."
-            )
-
-            # Conservative token estimation
-            return len(messages) * 100
+            _LOGGER.error("Error updating data: %s", err, exc_info=True)
+            return self._get_safe_initial_state()
 
+    # ------------------------------------------------------------------
+    # Question processing
+    # ------------------------------------------------------------------
     async def async_ask_question(
         self,
         question: str,
@@ -257,299 +211,220 @@ class HATextAICoordinator(DataUpdateCoordinator):
         max_tokens: Optional[int] = None,
         system_prompt: Optional[str] = None,
         context_messages: Optional[int] = None,
+        structured_output: bool = False,
+        json_schema: Optional[str] = None,
     ) -> dict:
-        """
-        Process a question with optional parameters.
+        """Process question with context management."""
+        if self.client is None:
+            raise HomeAssistantError("AI client not initialized")
 
-        This method is a direct wrapper around async_process_question,
-        allowing flexible AI interaction with optional model, temperature,
-        and context customization.
-
-        Args:
-            question: The input question or prompt
-            model: Optional AI model to use
-            temperature: Optional response creativity level
-            max_tokens: Optional maximum response length
-            system_prompt: Optional system-level instruction
-            context_messages: Optional number of context messages to include
-
-        Returns:
-            Full response dictionary from the AI
-        """
-        return await self.async_process_question(
-            question, model, temperature, max_tokens, system_prompt, context_messages
-        )
-
-    async def async_process_question(
-            self,
-            question: str,
-            model: Optional[str] = None,
-            temperature: Optional[float] = None,
-            max_tokens: Optional[int] = None,
-            system_prompt: Optional[str] = None,
-            context_messages: Optional[int] = None,
-        ) -> dict:
-            """
-            Enhanced question processing with intelligent token management.
-            """
+        async with self._request_lock:
             try:
                 self._is_processing = True
                 await self.async_update_ha_state()
 
-                temp_context_messages = context_messages or self.context_messages
-                temp_model = model or self.model
-                temp_temperature = temperature or self.temperature
-                temp_max_tokens = max_tokens or self.max_tokens
-                temp_system_prompt = system_prompt or self._system_prompt
+                temp_context = context_messages if context_messages is not None else self.context_messages
+                temp_model = model if model is not None else self.model
+                temp_temperature = temperature if temperature is not None else self.temperature
+                temp_max_tokens = max_tokens if max_tokens is not None else self.max_tokens
+                temp_system_prompt = system_prompt if system_prompt is not None else self._system_prompt
 
-                # Start timing
                 start_time = dt_util.utcnow()
 
-                # Prepare messages with system prompt
                 messages = []
                 if temp_system_prompt:
                     messages.append({"role": "system", "content": temp_system_prompt})
 
-                # Context history management
-                context_history = self._conversation_history[-temp_context_messages:]
-
-                # Comprehensive token calculation
-                context_tokens = self._calculate_context_tokens(
-                    [{"content": entry["question"]} for entry in context_history] +
-                    [{"content": entry["response"]} for entry in context_history] +
-                    [{"content": question}],
-                    temp_model
-                )
-
-                # Dynamic token allocation
-                available_tokens = max(0, temp_max_tokens - context_tokens)
-
-                # Context trimming if over token limit
-                if context_tokens > temp_max_tokens:
-                    _LOGGER.warning(
-                        f"Token limit exceeded. "
-                        f"Context: {context_tokens}, "
-                        f"Max: {temp_max_tokens}"
-                    )
-
-                    # Intelligent context reduction
-                    while context_tokens > temp_max_tokens // 2 and context_history:
-                        context_history.pop(0)
-                        context_tokens = self._calculate_context_tokens(
-                            [{"content": entry["question"]} for entry in context_history] +
-                            [{"content": entry["response"]} for entry in context_history] +
-                            [{"content": question}],
-                            temp_model
-                        )
-
-                # Rebuild messages with trimmed context
+                context_history = self._conversation_history[-temp_context:]
                 for entry in context_history:
                     messages.append({"role": "user", "content": entry["question"]})
                     messages.append({"role": "assistant", "content": entry["response"]})
 
                 messages.append({"role": "user", "content": question})
 
-                # Detailed token logging
-                _LOGGER.debug(
-                    f"Token Analysis: "
-                    f"Context Tokens: {context_tokens}, "
-                    f"Max Tokens: {temp_max_tokens}, "
-                    f"Available Tokens: {available_tokens}"
+                response = await self._send_to_api(
+                    question=question,
+                    model=temp_model,
+                    messages=messages,
+                    temperature=temp_temperature,
+                    max_tokens=temp_max_tokens,
+                    structured_output=structured_output,
+                    json_schema=json_schema,
                 )
 
-                # Prepare API call with dynamic token management
-                kwargs = {
-                    "model": temp_model,
-                    "temperature": temp_temperature,
-                    "max_tokens": min(temp_max_tokens, available_tokens),
-                    "messages": messages,
-                }
-
-                # Process message
-                response = await self.async_process_message(question, **kwargs)
-
-                # Update metrics
-                end_time = dt_util.utcnow()
-                latency = (end_time - start_time).total_seconds()
-                self._update_metrics(latency, response)
-
-                # Update history
-                self._update_history(question, response)
+                latency = (dt_util.utcnow() - start_time).total_seconds()
+                await self._metrics.update_metrics(latency, response)
+                await self._history.update_history(question, response)
 
                 return response
 
             except Exception as err:
-                self._handle_error(err)
+                error_details = await self._metrics.handle_error(err, self.model)
+                if error_details.get("is_connection_error"):
+                    self.endpoint_status = "unavailable"
+                self.last_response = error_details
                 raise HomeAssistantError(f"Failed to process question: {err}")
 
             finally:
                 self._is_processing = False
                 await self.async_update_ha_state()
 
-    async def async_process_message(self, question: str, **kwargs) -> dict:
-        """Process message using the AI client."""
+    async def _send_to_api(
+        self,
+        question: str,
+        model: str,
+        messages: List[Dict[str, str]],
+        temperature: float,
+        max_tokens: int,
+        structured_output: bool = False,
+        json_schema: Optional[str] = None,
+    ) -> dict:
+        """Send request to AI provider and return structured response.
+
+        Note: timeout is handled by APIClient via aiohttp ClientTimeout.
+        No additional asyncio.timeout wrapper to avoid dual timeout stacking.
+        """
         try:
-            if self.is_anthropic:
-                response = await self._process_anthropic_message(question, **kwargs)
-            else:
-                response = await self._process_openai_message(question, **kwargs)
+            response = await self.client.create(
+                model=model,
+                messages=messages,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                structured_output=structured_output,
+                json_schema=json_schema,
+            )
+
+            # Reset error state on success
+            self._is_rate_limited = False
+            self.endpoint_status = "ready"
+
+            timestamp = dt_util.utcnow().isoformat()
+            content = response["choices"][0]["message"]["content"]
+            tokens = {
+                "prompt": response["usage"]["prompt_tokens"],
+                "completion": response["usage"]["completion_tokens"],
+                "total": response["usage"]["total_tokens"],
+            }
 
             self.last_response = {
-                "timestamp": dt_util.utcnow().isoformat(),
+                "timestamp": timestamp,
                 "question": question,
-                "response": response["content"],
-                "model": kwargs.get("model", self.model),
+                "response": content,
+                "model": model,
                 "instance": self.instance_name,
+                "normalized_name": self.normalized_name,
                 "error": None,
             }
 
-            return response
+            return {
+                "content": content,
+                "tokens": tokens,
+                "model": model,
+                "timestamp": timestamp,
+                "instance": self.instance_name,
+                "question": question,
+                "success": True,
+            }
 
         except Exception as err:
-            self._handle_error(err)
+            _LOGGER.error("Error in API call: %s", err)
             raise
 
-    async def _process_anthropic_message(self, question: str, **kwargs) -> dict:
-        """Process message using Anthropic API."""
-        try:
-            _LOGGER.debug(f"Anthropic API call: model={kwargs['model']}, max_tokens={kwargs['max_tokens']}")
-            response = await self.client.messages.create(
-                model=kwargs["model"],
-                max_tokens=kwargs["max_tokens"],
-                messages=kwargs["messages"],
-                temperature=kwargs["temperature"],
-            )
-            _LOGGER.debug(f"Anthropic response: tokens={response.usage}")
-            return {
-                "content": response.content[0].text,
-                "tokens": {
-                    "prompt": response.usage.input_tokens,
-                    "completion": response.usage.output_tokens,
-                    "total": response.usage.input_tokens + response.usage.output_tokens,
-                },
-            }
-        except Exception as e:
-            _LOGGER.error(f"Anthropic API error: {str(e)}")
-            raise
-
-    async def _process_openai_message(self, question: str, **kwargs) -> dict:
-        """Process message using OpenAI API."""
-        try:
-            response = await self.client.create(
-                model=kwargs["model"],
-                messages=kwargs["messages"],
-                temperature=kwargs["temperature"],
-                max_tokens=kwargs["max_tokens"],
-            )
-
-            return {
-                "content": response["choices"][0]["message"]["content"],
-                "tokens": {
-                    "prompt": response["usage"]["prompt_tokens"],
-                    "completion": response["usage"]["completion_tokens"],
-                    "total": response["usage"]["total_tokens"],
-                },
-            }
-        except Exception as e:
-            _LOGGER.error(f"Error in OpenAI API call: {str(e)}")
-            raise
-
-    def _update_metrics(self, latency: float, response: dict) -> None:
-        """Update performance metrics."""
-        metrics = self._performance_metrics
-        tokens = response.get("tokens", {})
-
-        metrics["total_tokens"] += tokens.get("total", 0)
-        metrics["prompt_tokens"] += tokens.get("prompt", 0)
-        metrics["completion_tokens"] += tokens.get("completion", 0)
-        metrics["successful_requests"] += 1
-
-        metrics["average_latency"] = (
-            (metrics["average_latency"] * (metrics["successful_requests"] - 1) + latency)
-            / metrics["successful_requests"]
-        )
-        metrics["max_latency"] = max(metrics["max_latency"], latency)
-        metrics["min_latency"] = min(metrics["min_latency"], latency)
-
-    def _update_history(self, question: str, response: dict) -> None:
-        """Update conversation history."""
-        self._conversation_history.append(
-            {
-                "timestamp": dt_util.utcnow().isoformat(),
-                "question": question,
-                "response": response["content"],
-            }
-        )
-
-        while len(self._conversation_history) > self.max_history_size:
-            self._conversation_history.pop(0)
-
-    def _handle_error(self, error: Exception) -> None:
-        """
-        Enhanced error handling with comprehensive diagnostics.
-
-        Captures detailed error information, tracks error metrics,
-        and provides context for troubleshooting AI processing issues.
-        """
-        self._performance_metrics["total_errors"] += 1
-        self._performance_metrics["failed_requests"] += 1
-
-        error_details = {
-            "timestamp": dt_util.utcnow().isoformat(),
-            "model": self.model,
-            "instance": self.instance_name,
-            "error_message": str(error),
-            "error_type": type(error).__name__,
-            "traceback": traceback.format_exc() if _LOGGER.isEnabledFor(logging.DEBUG) else None,
-        }
-
-        # Specific error type handling
-        error_mapping = {
-            HomeAssistantError: {"is_ha_error": True},
-            ConnectionError: {
-                "is_connection_error": True,
-                "is_rate_limited": True
-            },
-            TimeoutError: {"is_timeout": True},
-            PermissionError: {"is_permission_denied": True},
-            ValueError: {"is_validation_error": True}
-        }
-
-        for error_type, error_flags in error_mapping.items():
-            if isinstance(error, error_type):
-                error_details.update(error_flags)
-                break
-
-        # Update system state based on error type
-        if error_details.get("is_rate_limited"):
-            self._is_rate_limited = True
-            _LOGGER.warning(f"Rate limit detected for {self.instance_name}")
-
-        if error_details.get("is_connection_error"):
-            self.endpoint_status = "unavailable"
-
-        self.last_response = error_details
-        _LOGGER.error(f"AI Processing Error: {error_details}")
-
-        # Optional: Add more sophisticated error tracking or notification logic
-        if _LOGGER.isEnabledFor(logging.DEBUG):
-            _LOGGER.debug(f"Full Error Traceback: {error_details['traceback']}")
-
+    # ------------------------------------------------------------------
+    # History / prompt delegation
+    # ------------------------------------------------------------------
     async def async_clear_history(self) -> None:
         """Clear conversation history."""
-        self._conversation_history = []
+        await self._history.async_clear_history()
         await self.async_update_ha_state()
 
-    async def async_get_history(self) -> List[Dict[str, str]]:
-        """Get conversation history."""
-        return self._conversation_history
+    async def async_get_history(
+        self,
+        limit: Optional[int] = None,
+        filter_model: Optional[str] = None,
+        start_date: Optional[str] = None,
+        include_metadata: bool = False,
+        sort_order: str = "newest",
+    ) -> List[Dict[str, Any]]:
+        """Get conversation history with optional filtering."""
+        return await self._history.async_get_history(
+            limit=limit,
+            filter_model=filter_model,
+            start_date=start_date,
+            include_metadata=include_metadata,
+            sort_order=sort_order,
+            default_model=self.model,
+        )
 
     async def async_set_system_prompt(self, prompt: str) -> None:
         """Set system prompt."""
         self._system_prompt = prompt
         await self.async_update_ha_state()
 
-    async def async_shutdown(self) -> None:
-        """Shutdown coordinator."""
-        _LOGGER.debug(f"Shutting down coordinator for {self.instance_name}")
-        self.hass.data[DOMAIN].pop(self.instance_name, None)
+    # ------------------------------------------------------------------
+    # Internal helpers
+    # ------------------------------------------------------------------
+    def _get_current_state(self) -> str:
+        if self._is_processing:
+            return STATE_PROCESSING
+        if self._is_rate_limited:
+            return STATE_RATE_LIMITED
+        if self._is_maintenance:
+            return STATE_MAINTENANCE
+        if self.last_response.get("error") or self.last_response.get("error_message"):
+            return STATE_ERROR
+        return STATE_READY
+
+    def _get_safe_initial_state(self) -> Dict[str, Any]:
+        return {
+            "state": STATE_ERROR,
+            "metrics": {},
+            "last_response": self.last_response,
+            "is_processing": False,
+            "is_rate_limited": False,
+            "is_maintenance": False,
+            "endpoint_status": "error",
+            "uptime": self._calculate_uptime(),
+            "system_prompt": None,
+            "history_size": 0,
+            "conversation_history": [],
+            "history_info": {
+                "total_entries": 0,
+                "displayed_entries": 0,
+            },
+            "normalized_name": self.normalized_name,
+        }
+
+    def _get_sanitized_last_response(self) -> Dict[str, Any]:
+        """Get sanitized version of last response with truncation."""
+        response = self.last_response.copy()
+
+        for field in ("response", "question"):
+            if field in response and response[field]:
+                original = response[field]
+                truncated = len(original) > 4096
+                response[field] = (
+                    original[:4096] + TRUNCATION_INDICATOR if truncated else original
+                )
+                response[f"is_{field}_truncated"] = truncated
+                response[f"full_{field}_length"] = len(original)
+
+        return response
+
+    def _calculate_uptime(self) -> float:
+        return (dt_util.utcnow() - self._start_time).total_seconds()
+
+    def _get_truncated_system_prompt(self) -> Optional[str]:
+        if not self._system_prompt:
+            return None
+        if len(self._system_prompt) <= 4096:
+            return self._system_prompt
+        return self._system_prompt[:4096] + TRUNCATION_INDICATOR
+
+    @staticmethod
+    def _validate_update_data(data: Dict[str, Any]) -> None:
+        for key in ("state", "metrics", "last_response"):
+            if key not in data:
+                raise ValueError(f"Missing required key: {key}")
+        if not isinstance(data["metrics"], dict):
+            raise ValueError("Invalid metrics format")

custom_components/ha_text_ai/history.py

@@ -0,0 +1,459 @@
+"""
+History management for HA Text AI integration.
+
+@license: PolyForm Noncommercial 1.0.0 (https://polyformproject.org/licenses/noncommercial/1.0.0)
+@author: SMKRV
+@github: https://github.com/smkrv/ha-text-ai
+@source: https://github.com/smkrv/ha-text-ai
+"""
+from __future__ import annotations
+
+import json
+import logging
+import os
+import shutil
+import traceback
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+
+import aiofiles
+
+from homeassistant.core import HomeAssistant
+from homeassistant.util import dt as dt_util
+
+from .const import (
+    ABSOLUTE_MAX_HISTORY_SIZE,
+    MAX_ATTRIBUTE_SIZE,
+    MAX_HISTORY_FILE_SIZE,
+    TRUNCATION_INDICATOR,
+)
+
+# Per-entry storage cap (32KB per field) to prevent disk exhaustion
+MAX_STORED_FIELD_SIZE = 32 * 1024
+MAX_ARCHIVE_FILES = 3
+
+_LOGGER = logging.getLogger(__name__)
+
+
+class AsyncFileHandler:
+    """Async context manager for file operations."""
+
+    def __init__(self, file_path: str, mode: str = "a"):
+        self.file_path = file_path
+        self.mode = mode
+
+    async def __aenter__(self):
+        self.file = await aiofiles.open(self.file_path, self.mode)
+        return self.file
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        await self.file.close()
+
+
+class HistoryManager:
+    """Manages conversation history for an instance."""
+
+    def __init__(
+        self,
+        hass: HomeAssistant,
+        instance_name: str,
+        normalized_name: str,
+        history_dir: str,
+        max_history_size: int,
+    ) -> None:
+        self.hass = hass
+        self.instance_name = instance_name
+        self.normalized_name = normalized_name
+        self._history_dir = history_dir
+        self.max_history_size = min(
+            max(1, max_history_size), ABSOLUTE_MAX_HISTORY_SIZE
+        )
+        self._history_file = os.path.join(
+            history_dir, f"{normalized_name}_history.json"
+        )
+        self._max_history_file_size = MAX_HISTORY_FILE_SIZE
+        self._conversation_history: List[Dict[str, Any]] = []
+
+    @property
+    def conversation_history(self) -> List[Dict[str, Any]]:
+        return self._conversation_history
+
+    @property
+    def history_size(self) -> int:
+        return len(self._conversation_history)
+
+    async def async_initialize(self) -> None:
+        """Initialize history: directories, file, migration."""
+        await self._create_history_dir()
+        await self._check_history_directory()
+        await self._initialize_history_file()
+        await self._migrate_history_from_txt_to_json()
+
+    async def _file_exists(self, path: str) -> bool:
+        try:
+            return await self.hass.async_add_executor_job(os.path.exists, path)
+        except Exception as e:
+            _LOGGER.error("Error checking file existence for %s: %s", path, e)
+            return False
+
+    async def _create_history_dir(self) -> None:
+        try:
+            await self.hass.async_add_executor_job(
+                os.makedirs, self._history_dir, 0o755, True
+            )
+        except PermissionError:
+            _LOGGER.error("Permission denied creating history directory: %s", self._history_dir)
+            raise
+        except OSError as e:
+            _LOGGER.error("Error creating history directory %s: %s", self._history_dir, e)
+            raise
+
+    async def _check_history_directory(self) -> None:
+        """Check history directory permissions and writability."""
+        try:
+            test_file_path = os.path.join(self._history_dir, ".write_test")
+            await self.hass.async_add_executor_job(
+                self._sync_test_directory_write, test_file_path
+            )
+        except PermissionError:
+            _LOGGER.error("No write permissions for history directory: %s", self._history_dir)
+        except Exception as e:
+            _LOGGER.error("Error checking history directory: %s", e)
+
+    @staticmethod
+    def _sync_test_directory_write(test_file_path: str) -> None:
+        try:
+            os.makedirs(os.path.dirname(test_file_path), mode=0o755, exist_ok=True)
+            with open(test_file_path, "w") as f:
+                f.write("Permission test")
+            os.remove(test_file_path)
+        except Exception as e:
+            _LOGGER.error("Directory write test failed: %s", e)
+
+    async def _initialize_history_file(self) -> None:
+        """Initialize history file and load existing history."""
+        try:
+            if await self._file_exists(self._history_file):
+                async with AsyncFileHandler(self._history_file, "r") as f:
+                    content = await f.read()
+                    if content:
+                        history = json.loads(content)
+                        if isinstance(history, list):
+                            self._conversation_history = history[
+                                -self.max_history_size :
+                            ]
+                            _LOGGER.debug(
+                                "Loaded %d history entries for %s",
+                                len(self._conversation_history),
+                                self.instance_name,
+                            )
+            else:
+                async with AsyncFileHandler(self._history_file, "w") as f:
+                    await f.write(json.dumps([]))
+
+            await self._check_history_size()
+        except Exception as e:
+            _LOGGER.error("Could not initialize history file: %s", e)
+            _LOGGER.debug(traceback.format_exc())
+
+    async def update_history(self, question: str, response: dict) -> None:
+        """Update conversation history.
+
+        In-memory history stores full text for context retrieval.
+        On-disk storage caps per-field size to prevent disk exhaustion.
+        Display truncation is handled by get_limited_history().
+        """
+        try:
+            content = response.get("content", "")
+            history_entry = {
+                "timestamp": dt_util.utcnow().isoformat(),
+                "question": question[:MAX_STORED_FIELD_SIZE],
+                "response": content[:MAX_STORED_FIELD_SIZE],
+            }
+
+            self._conversation_history.append(history_entry)
+
+            while len(self._conversation_history) > self.max_history_size:
+                self._conversation_history.pop(0)
+
+            await self._save_history_to_file()
+        except Exception as e:
+            _LOGGER.error("Error updating history: %s", e)
+            _LOGGER.debug(traceback.format_exc())
+
+    async def _save_history_to_file(self) -> None:
+        """Serialize in-memory history to file with rotation if needed."""
+        try:
+            data = json.dumps(self._conversation_history, indent=2)
+            data_size = len(data.encode("utf-8"))
+
+            if data_size > MAX_HISTORY_FILE_SIZE:
+                await self._rotate_history()
+
+            async with AsyncFileHandler(self._history_file, "w") as f:
+                await f.write(data)
+        except Exception as e:
+            _LOGGER.error("Error writing history file: %s", e)
+            _LOGGER.debug(traceback.format_exc())
+
+    async def _check_history_size(self) -> None:
+        if len(self._conversation_history) > self.max_history_size:
+            _LOGGER.warning(
+                "History size (%d) exceeds maximum (%d). Trimming...",
+                len(self._conversation_history), self.max_history_size,
+            )
+            self._conversation_history = self._conversation_history[
+                -self.max_history_size :
+            ]
+
+    async def _check_file_size(self, file_path: str) -> int:
+        try:
+            if await self._file_exists(file_path):
+                return await self.hass.async_add_executor_job(
+                    os.path.getsize, file_path
+                )
+            return 0
+        except Exception as e:
+            _LOGGER.error("Error checking file size for %s: %s", file_path, e)
+            return 0
+
+    async def _rotate_history(self) -> None:
+        try:
+            _LOGGER.debug("Starting history rotation for %s", self._history_file)
+            await self._rotate_history_files()
+        except Exception as e:
+            _LOGGER.error("Error rotating history: %s", e)
+            _LOGGER.debug(traceback.format_exc())
+
+    async def _rotate_history_files(self) -> None:
+        """Rotate history files with size validation."""
+        try:
+            if await self._file_exists(self._history_file):
+                current_size = await self._check_file_size(self._history_file)
+
+                if current_size > MAX_HISTORY_FILE_SIZE:
+                    _LOGGER.info(
+                        "Rotating history file. Current size: %d, Max: %d",
+                        current_size, MAX_HISTORY_FILE_SIZE,
+                    )
+
+                    archive_file = os.path.join(
+                        self._history_dir,
+                        f"{self.normalized_name}_history_{dt_util.utcnow().strftime('%Y%m%d_%H%M%S')}.json",
+                    )
+
+                    await self.hass.async_add_executor_job(
+                        shutil.move, self._history_file, archive_file
+                    )
+
+                    async with AsyncFileHandler(self._history_file, "w") as f:
+                        await f.write(
+                            json.dumps(
+                                self._conversation_history[
+                                    -self.max_history_size :
+                                ],
+                                indent=2,
+                            )
+                        )
+
+                    _LOGGER.info("History file rotated to: %s", archive_file)
+
+                    # Clean up old archive files, keep only MAX_ARCHIVE_FILES
+                    await self._cleanup_archives()
+        except Exception as e:
+            _LOGGER.error("History rotation failed: %s", e)
+            _LOGGER.debug(traceback.format_exc())
+
+    async def _cleanup_archives(self) -> None:
+        """Remove old archive files beyond MAX_ARCHIVE_FILES."""
+        try:
+            prefix = f"{self.normalized_name}_history_"
+
+            def find_archives():
+                archives = []
+                for f in os.listdir(self._history_dir):
+                    if f.startswith(prefix) and f.endswith(".json") and f != os.path.basename(self._history_file):
+                        archives.append(os.path.join(self._history_dir, f))
+                archives.sort()
+                return archives
+
+            archives = await self.hass.async_add_executor_job(find_archives)
+            if len(archives) > MAX_ARCHIVE_FILES:
+                for old_file in archives[:-MAX_ARCHIVE_FILES]:
+                    await self.hass.async_add_executor_job(os.remove, old_file)
+                    _LOGGER.debug("Removed old archive: %s", old_file)
+        except Exception as e:
+            _LOGGER.warning("Archive cleanup error: %s", e)
+
+    async def _migrate_history_from_txt_to_json(self) -> None:
+        """Migrate old .txt history to .json format."""
+        try:
+            old_history_file = os.path.join(
+                self._history_dir, f"{self.normalized_name}_history.txt"
+            )
+
+            if not await self._file_exists(old_history_file):
+                return
+
+            # Skip migration if JSON history already has entries
+            if self._conversation_history:
+                _LOGGER.debug(
+                    "JSON history already has %d entries for %s, skipping txt migration",
+                    len(self._conversation_history), self.instance_name,
+                )
+                return
+
+            _LOGGER.info(
+                "Found old history file for %s, migrating to JSON", self.instance_name
+            )
+
+            history_entries = []
+            async with AsyncFileHandler(old_history_file, "r") as f:
+                content = await f.read()
+
+            for line in content.split("\n"):
+                if not line or line.startswith("History initialized at:"):
+                    continue
+                try:
+                    parts = line.split(": ", 1)
+                    if len(parts) != 2:
+                        continue
+                    timestamp = parts[0]
+                    content_parts = parts[1].split(" - ")
+                    if len(content_parts) != 2:
+                        continue
+                    question = content_parts[0].replace("Question: ", "")
+                    response = content_parts[1].replace("Response: ", "")
+                    history_entries.append(
+                        {
+                            "timestamp": timestamp,
+                            "question": question,
+                            "response": response,
+                        }
+                    )
+                except Exception as e:
+                    _LOGGER.warning("Error parsing history line: %s. Error: %s", line, e)
+                    continue
+
+            if history_entries:
+                async with AsyncFileHandler(self._history_file, "w") as f:
+                    await f.write(json.dumps(history_entries, indent=2))
+
+                backup_file = old_history_file + ".backup"
+                await self.hass.async_add_executor_job(
+                    shutil.move, old_history_file, backup_file
+                )
+
+                _LOGGER.info(
+                    "Migrated %d entries from txt to JSON for %s. Old file: %s",
+                    len(history_entries), self.instance_name, backup_file,
+                )
+
+                self._conversation_history = history_entries
+        except Exception as e:
+            _LOGGER.error("Error during history migration for %s: %s", self.instance_name, e)
+            _LOGGER.debug(traceback.format_exc())
+
+    async def async_clear_history(self) -> None:
+        """Clear conversation history."""
+        try:
+            self._conversation_history = []
+            if await self._file_exists(self._history_file):
+                await self.hass.async_add_executor_job(os.remove, self._history_file)
+            _LOGGER.info("History for %s cleared", self.instance_name)
+        except Exception as e:
+            _LOGGER.error("Error clearing history: %s", e)
+            _LOGGER.debug(traceback.format_exc())
+
+    async def async_get_history(
+        self,
+        limit: Optional[int] = None,
+        filter_model: Optional[str] = None,
+        start_date: Optional[str] = None,
+        include_metadata: bool = False,
+        sort_order: str = "newest",
+        default_model: str = "",
+    ) -> List[Dict[str, Any]]:
+        """Get conversation history with optional filtering and sorting."""
+        try:
+            history = self._conversation_history.copy()
+
+            if filter_model:
+                history = [
+                    entry for entry in history if entry.get("model") == filter_model
+                ]
+
+            if start_date:
+                try:
+                    start_dt = datetime.fromisoformat(
+                        start_date.replace("Z", "+00:00")
+                    )
+                    history = [
+                        entry
+                        for entry in history
+                        if datetime.fromisoformat(
+                            entry["timestamp"].replace("Z", "+00:00")
+                        )
+                        >= start_dt
+                    ]
+                except (ValueError, KeyError) as e:
+                    _LOGGER.warning("Invalid start_date format: %s. Error: %s", start_date, e)
+
+            if sort_order == "oldest":
+                history.sort(key=lambda x: x.get("timestamp", ""))
+            else:
+                history.sort(key=lambda x: x.get("timestamp", ""), reverse=True)
+
+            if limit and limit > 0:
+                history = history[:limit]
+
+            if include_metadata:
+                enriched = []
+                for entry in history:
+                    enriched_entry = dict(entry)
+                    enriched_entry["metadata"] = {
+                        "entry_size": len(str(entry)),
+                        "question_length": len(entry.get("question", "")),
+                        "response_length": len(entry.get("response", "")),
+                        "model_used": entry.get("model", default_model),
+                        "instance": self.instance_name,
+                    }
+                    enriched.append(enriched_entry)
+                return enriched
+
+            return history
+        except Exception as e:
+            _LOGGER.error("Error getting history: %s", e)
+            return []
+
+    def get_limited_history(self, max_display: int = 5) -> Dict[str, Any]:
+        """Get limited conversation history for sensor attributes.
+
+        Returns last `max_display` entries with truncated text for HA state.
+        """
+        recent = self._conversation_history[-max_display:]
+        limited_history = [
+            {
+                "timestamp": entry["timestamp"],
+                "question": self._truncate_text(entry["question"], 4096),
+                "response": self._truncate_text(entry["response"], 4096),
+            }
+            for entry in recent
+        ]
+
+        return {
+            "entries": limited_history,
+            "info": {
+                "total_entries": len(self._conversation_history),
+                "displayed_entries": len(limited_history),
+            },
+        }
+
+    @staticmethod
+    def _truncate_text(text: str, max_length: int = MAX_ATTRIBUTE_SIZE) -> str:
+        """Safely truncate text to maximum length with indicator."""
+        if not text:
+            return ""
+        if len(text) <= max_length:
+            return text
+        return text[:max_length] + TRUNCATION_INDICATOR

custom_components/ha_text_ai/manifest.json

@@ -2,7 +2,6 @@
   "domain": "ha_text_ai",
   "name": "HA Text AI",
   "after_dependencies": ["http"],
-  "bluetooth": [],
   "codeowners": ["@smkrv"],
   "config_flow": true,
   "dependencies": [],
@@ -11,18 +10,10 @@
   "iot_class": "cloud_polling",
   "issue_tracker": "https://github.com/smkrv/ha-text-ai/issues",
   "loggers": ["custom_components.ha_text_ai"],
-  "mqtt": [],
-  "quality_scale": "silver",
   "requirements": [
-    "openai>=1.12.0",
-    "anthropic>=0.8.0",
-    "aiohttp>=3.8.0",
-    "async-timeout>=4.0.0",
-    "certifi>=2024.2.2"
+    "aiofiles>=23.0.0",
+    "google-genai>=1.16.0"
   ],
   "single_config_entry": false,
-  "ssdp": [],
-  "usb": [],
-  "version": "2.0.2-alpha",
-  "zeroconf": []
+  "version": "2.4.1"
 }

custom_components/ha_text_ai/metrics.py

@@ -0,0 +1,164 @@
+"""
+Metrics management for HA Text AI integration.
+
+@license: PolyForm Noncommercial 1.0.0 (https://polyformproject.org/licenses/noncommercial/1.0.0)
+@author: SMKRV
+@github: https://github.com/smkrv/ha-text-ai
+@source: https://github.com/smkrv/ha-text-ai
+"""
+from __future__ import annotations
+
+import json
+import logging
+import os
+import re
+import traceback
+from typing import Any, Dict
+
+from homeassistant.core import HomeAssistant
+from homeassistant.exceptions import HomeAssistantError
+from homeassistant.util import dt as dt_util
+
+_LOGGER = logging.getLogger(__name__)
+
+DEFAULT_METRICS: Dict[str, Any] = {
+    "total_tokens": 0,
+    "prompt_tokens": 0,
+    "completion_tokens": 0,
+    "successful_requests": 0,
+    "failed_requests": 0,
+    "total_errors": 0,
+    "average_latency": 0,
+    "max_latency": 0,
+    "min_latency": 0,
+}
+
+
+class MetricsManager:
+    """Manages performance metrics for an instance."""
+
+    def __init__(
+        self,
+        hass: HomeAssistant,
+        instance_name: str,
+        metrics_file: str,
+    ) -> None:
+        self.hass = hass
+        self.instance_name = instance_name
+        self._metrics_file = metrics_file
+        self._performance_metrics: Dict[str, Any] = DEFAULT_METRICS.copy()
+
+    @property
+    def metrics(self) -> Dict[str, Any]:
+        return self._performance_metrics
+
+    async def async_initialize(self) -> None:
+        """Load metrics from storage or create defaults."""
+        loaded = await self._load_metrics()
+        self._performance_metrics = loaded or DEFAULT_METRICS.copy()
+
+    async def _load_metrics(self) -> Dict[str, Any] | None:
+        try:
+            exists = await self.hass.async_add_executor_job(
+                os.path.exists, self._metrics_file
+            )
+            if exists:
+                def read_metrics():
+                    with open(self._metrics_file, "r") as f:
+                        try:
+                            return json.load(f)
+                        except json.JSONDecodeError:
+                            _LOGGER.warning("Metrics file corrupted, creating new")
+                            return None
+
+                return await self.hass.async_add_executor_job(read_metrics)
+        except Exception as e:
+            _LOGGER.warning("Failed to load metrics: %s", e)
+        return None
+
+    async def _save_metrics(self) -> None:
+        try:
+            def write_metrics():
+                with open(self._metrics_file, "w") as f:
+                    json.dump(self._performance_metrics, f)
+
+            await self.hass.async_add_executor_job(write_metrics)
+        except Exception as e:
+            _LOGGER.warning("Failed to save metrics: %s", e)
+
+    async def update_metrics(self, latency: float, response: dict) -> None:
+        """Update performance metrics after a successful request."""
+        metrics = self._performance_metrics
+        tokens = response.get("tokens", {})
+
+        metrics["total_tokens"] += tokens.get("total", 0)
+        metrics["prompt_tokens"] += tokens.get("prompt", 0)
+        metrics["completion_tokens"] += tokens.get("completion", 0)
+        metrics["successful_requests"] += 1
+
+        metrics["average_latency"] = (
+            (metrics["average_latency"] * (metrics["successful_requests"] - 1) + latency)
+            / metrics["successful_requests"]
+        )
+        metrics["max_latency"] = max(metrics["max_latency"], latency)
+        if metrics["min_latency"] == 0:
+            metrics["min_latency"] = latency
+        else:
+            metrics["min_latency"] = min(metrics["min_latency"], latency)
+
+        await self._save_metrics()
+
+    async def get_current_metrics(self) -> Dict[str, Any]:
+        """Get current performance metrics."""
+        return self._performance_metrics.copy()
+
+    async def handle_error(
+        self,
+        error: Exception,
+        model: str,
+    ) -> Dict[str, Any]:
+        """Record an error in metrics and return error details."""
+        self._performance_metrics["total_errors"] += 1
+        self._performance_metrics["failed_requests"] += 1
+        await self._save_metrics()
+
+        error_msg = str(error)
+        # Strip URLs, API keys, tokens, and query parameters from error messages
+        error_msg = re.sub(r'https?://\S+', '[URL]', error_msg)
+        error_msg = re.sub(r'[?&]key=[^\s&]+', '?key=***', error_msg)
+        error_msg = re.sub(r'AIza[A-Za-z0-9_-]+', '***', error_msg)
+        error_msg = re.sub(r'Bearer\s+\S+', 'Bearer ***', error_msg)
+        error_msg = re.sub(r'sk-[A-Za-z0-9_-]{20,}', '***', error_msg)
+        error_msg = re.sub(r'x-api-key:\s*\S+', 'x-api-key: ***', error_msg, flags=re.IGNORECASE)
+        if len(error_msg) > 256:
+            error_msg = error_msg[:256] + "..."
+
+        error_details: Dict[str, Any] = {
+            "timestamp": dt_util.utcnow().isoformat(),
+            "model": model,
+            "instance": self.instance_name,
+            "error_message": error_msg,
+            "error_type": type(error).__name__,
+            "traceback": traceback.format_exc()
+            if _LOGGER.isEnabledFor(logging.DEBUG)
+            else None,
+        }
+
+        error_mapping = {
+            HomeAssistantError: {"is_ha_error": True},
+            ConnectionError: {"is_connection_error": True},
+            TimeoutError: {"is_timeout": True},
+            PermissionError: {"is_permission_denied": True},
+            ValueError: {"is_validation_error": True},
+        }
+
+        for error_type, error_flags in error_mapping.items():
+            if isinstance(error, error_type):
+                error_details.update(error_flags)
+                break
+
+        _LOGGER.error("AI Processing Error: %s", error_details)
+        if _LOGGER.isEnabledFor(logging.DEBUG):
+            _LOGGER.debug("Full Error Traceback: %s", error_details.get("traceback"))
+
+        return error_details

custom_components/ha_text_ai/providers.py

@@ -0,0 +1,97 @@
+"""
+Provider registry for HA Text AI integration.
+
+Centralizes provider-specific configuration to avoid dispatch duplication
+across __init__.py, config_flow.py, and api_client.py.
+
+@license: PolyForm Noncommercial 1.0.0 (https://polyformproject.org/licenses/noncommercial/1.0.0)
+@author: SMKRV
+@github: https://github.com/smkrv/ha-text-ai
+@source: https://github.com/smkrv/ha-text-ai
+"""
+from __future__ import annotations
+
+from typing import Any
+
+from .const import (
+    API_PROVIDER_OPENAI,
+    API_PROVIDER_ANTHROPIC,
+    API_PROVIDER_DEEPSEEK,
+    API_PROVIDER_GEMINI,
+    DEFAULT_MODEL,
+    DEFAULT_ANTHROPIC_MODEL,
+    DEFAULT_DEEPSEEK_MODEL,
+    DEFAULT_GEMINI_MODEL,
+    DEFAULT_OPENAI_ENDPOINT,
+    DEFAULT_ANTHROPIC_ENDPOINT,
+    DEFAULT_DEEPSEEK_ENDPOINT,
+    DEFAULT_GEMINI_ENDPOINT,
+)
+
+PROVIDER_REGISTRY: dict[str, dict[str, Any]] = {
+    API_PROVIDER_OPENAI: {
+        "default_model": DEFAULT_MODEL,
+        "default_endpoint": DEFAULT_OPENAI_ENDPOINT,
+        "auth_header": "Authorization",
+        "auth_prefix": "Bearer ",
+        "check_path": "/models",
+    },
+    API_PROVIDER_ANTHROPIC: {
+        "default_model": DEFAULT_ANTHROPIC_MODEL,
+        "default_endpoint": DEFAULT_ANTHROPIC_ENDPOINT,
+        "auth_header": "x-api-key",
+        "auth_prefix": "",
+        "check_path": "/v1/models",
+        "extra_headers": {
+            "anthropic-version": "2023-06-01",
+        },
+    },
+    API_PROVIDER_DEEPSEEK: {
+        "default_model": DEFAULT_DEEPSEEK_MODEL,
+        "default_endpoint": DEFAULT_DEEPSEEK_ENDPOINT,
+        "auth_header": "Authorization",
+        "auth_prefix": "Bearer ",
+        "check_path": "/models",
+    },
+    API_PROVIDER_GEMINI: {
+        "default_model": DEFAULT_GEMINI_MODEL,
+        "default_endpoint": DEFAULT_GEMINI_ENDPOINT,
+        "auth_header": "Authorization",
+        "auth_prefix": "Bearer ",
+        "check_path": None,  # Gemini does not support /models check
+    },
+}
+
+
+def get_provider_config(provider: str) -> dict[str, Any]:
+    """Get full provider configuration.
+
+    Raises ValueError for unknown providers to avoid sending
+    credentials to the wrong endpoint.
+    """
+    if provider not in PROVIDER_REGISTRY:
+        raise ValueError(f"Unknown API provider: {provider}")
+    return PROVIDER_REGISTRY[provider]
+
+
+def get_default_endpoint(provider: str) -> str:
+    """Get default API endpoint for a provider."""
+    return get_provider_config(provider)["default_endpoint"]
+
+
+def get_default_model(provider: str) -> str:
+    """Get default model for a provider."""
+    return get_provider_config(provider)["default_model"]
+
+
+def build_auth_headers(provider: str, api_key: str) -> dict[str, str]:
+    """Build authentication headers for a provider."""
+    config = get_provider_config(provider)
+    headers = {
+        "Content-Type": "application/json",
+        "Accept": "application/json",
+    }
+    headers[config["auth_header"]] = f"{config['auth_prefix']}{api_key}"
+    if "extra_headers" in config:
+        headers.update(config["extra_headers"])
+    return headers

custom_components/ha_text_ai/sensor.py

@@ -1,8 +1,16 @@
-"""Sensor platform for HA Text AI."""
+"""
+Sensor platform for HA Text AI.
+
+@license: PolyForm Noncommercial 1.0.0 (https://polyformproject.org/licenses/noncommercial/1.0.0)
+@author: SMKRV
+@github: https://github.com/smkrv/ha-text-ai
+@source: https://github.com/smkrv/ha-text-ai
+"""
+from __future__ import annotations
+
 import logging
 import math
 from typing import Any, Dict
-
 from homeassistant.components.sensor import (
     SensorEntity,
     SensorEntityDescription,
@@ -36,10 +44,8 @@ from .const import (
     ATTR_API_PROVIDER,
     ATTR_MODEL,
     ATTR_SYSTEM_PROMPT,
-    ATTR_API_STATUS,
     ATTR_RESPONSE,
     ATTR_QUESTION,
-    ATTR_CONVERSATION_HISTORY,
     METRIC_TOTAL_TOKENS,
     METRIC_PROMPT_TOKENS,
     METRIC_COMPLETION_TOKENS,
@@ -60,12 +66,19 @@ from .const import (
     ENTITY_ICON_PROCESSING,
     DEFAULT_NAME_PREFIX,
     CONF_MAX_HISTORY_SIZE,
+    VERSION,
 )
 
 from .coordinator import HATextAICoordinator
+from .utils import safe_log_data
 
 _LOGGER = logging.getLogger(__name__)
 
+# HA Recorder limit is 16384 bytes for state_attributes.
+# Budget per field to stay well within the limit.
+_ATTR_TEXT_LIMIT = 2048
+_ATTR_PROMPT_LIMIT = 512
+
 
 async def async_setup_entry(
     hass: HomeAssistant,
@@ -73,23 +86,23 @@ async def async_setup_entry(
     async_add_entities: AddEntitiesCallback,
 ) -> None:
     """Set up the HA Text AI sensor."""
-    _LOGGER.debug(f"Starting sensor setup for entry: {entry.entry_id}")
+    _LOGGER.debug("Starting sensor setup for entry: %s", entry.entry_id)
 
     try:
         coordinator = hass.data[DOMAIN][entry.entry_id]
-        _LOGGER.debug(f"Found coordinator for entry {entry.entry_id}")
+        _LOGGER.debug("Found coordinator for entry %s", entry.entry_id)
 
         instance_name = coordinator.instance_name
-        _LOGGER.debug(f"Setting up sensor with instance: {instance_name}")
+        _LOGGER.debug("Setting up sensor with instance: %s", instance_name)
 
         sensor = HATextAISensor(coordinator, entry)
-        _LOGGER.debug(f"Created sensor instance: {sensor.entity_id}")
+        _LOGGER.debug("Created sensor instance: %s", sensor.entity_id)
 
         async_add_entities([sensor], True)
-        _LOGGER.debug(f"Added sensor entity: {sensor.entity_id}")
+        _LOGGER.debug("Added sensor entity: %s", sensor.entity_id)
 
     except Exception as err:
-        _LOGGER.exception(f"Error setting up sensor: {err}")
+        _LOGGER.exception("Error setting up sensor: %s", err)
         raise
 
 class HATextAISensor(CoordinatorEntity, SensorEntity):
@@ -103,7 +116,7 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
         config_entry: ConfigEntry,
     ) -> None:
         """Initialize the sensor."""
-        _LOGGER.debug(f"Initializing sensor with config entry: {config_entry.data}")
+        _LOGGER.debug("Initializing sensor with config entry: %s", safe_log_data(dict(config_entry.data)))
 
         super().__init__(coordinator)
 
@@ -111,19 +124,20 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
         self._instance_name = coordinator.instance_name
         self._normalized_name = coordinator.normalized_name
 
-        _LOGGER.debug(f"Instance name: {self._instance_name}")
-        _LOGGER.debug(f"Normalized name: {self._normalized_name}")
+        _LOGGER.debug("Instance name: %s", self._instance_name)
+        _LOGGER.debug("Normalized name: %s", self._normalized_name)
 
         self._conversation_history = []
         self._system_prompt = None
 
-        self._attr_name = f"HA Text AI {self._instance_name}"
+        self._attr_has_entity_name = True
+        self._attr_name = self._instance_name
         self.entity_id = f"sensor.ha_text_ai_{self._normalized_name}"
-        self._attr_unique_id = f"{config_entry.entry_id}"
+        self._attr_unique_id = config_entry.entry_id
 
-        _LOGGER.debug(f"Created sensor with entity_id: {self.entity_id}")
-        _LOGGER.debug(f"Sensor name: {self._attr_name}")
-        _LOGGER.debug(f"Unique ID: {self._attr_unique_id}")
+        _LOGGER.debug("Created sensor with entity_id: %s", self.entity_id)
+        _LOGGER.debug("Sensor name: %s", self._attr_name)
+        _LOGGER.debug("Unique ID: %s", self._attr_unique_id)
 
         self.entity_description = SensorEntityDescription(
             key=f"ha_text_ai_{self._normalized_name.lower()}",
@@ -146,11 +160,12 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
             name=self._attr_name,
             manufacturer="Community",
             model=f"{model} ({api_provider} provider)",
-            sw_version="1.0.0",
+            sw_version=VERSION,
         )
 
         _LOGGER.debug(
-            f"Initialized sensor: {self.entity_id} for instance: {self._instance_name}"
+            "Initialized sensor: %s for instance: %s",
+            self.entity_id, self._instance_name,
         )
 
     @property
@@ -171,12 +186,29 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
 
     def _sanitize_attributes(self, attributes: Dict[str, Any]) -> Dict[str, Any]:
         """Sanitize all attributes for JSON serialization."""
-        return {
+        sanitized = {
             key: self._sanitize_value(value)
             for key, value in attributes.items()
             if value is not None
         }
 
+        # Log metrics for debugging
+        metrics_keys = [
+            METRIC_TOTAL_TOKENS,
+            METRIC_PROMPT_TOKENS,
+            METRIC_COMPLETION_TOKENS,
+            METRIC_SUCCESSFUL_REQUESTS,
+            METRIC_FAILED_REQUESTS,
+            METRIC_AVERAGE_LATENCY,
+            METRIC_MAX_LATENCY,
+            METRIC_MIN_LATENCY,
+        ]
+
+        metrics_values = {k: sanitized.get(k) for k in metrics_keys if k in sanitized}
+        _LOGGER.debug("Metrics for %s: %s", self.entity_id, metrics_values)
+
+        return sanitized
+
     @property
     def native_value(self) -> StateType:
         """Return the native value of the sensor."""
@@ -205,68 +237,63 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
 
         try:
             data = self.coordinator.data
+            metrics = data.get("metrics", {})
+
+            # Base attributes
             attributes = {
                 ATTR_MODEL: self._config_entry.data.get(CONF_MODEL, "Unknown"),
-                ATTR_API_PROVIDER: self._config_entry.data.get(
-                    CONF_API_PROVIDER, "Unknown"
-                ),
-                ATTR_API_STATUS: self._current_state,
-                ATTR_TOTAL_ERRORS: self._error_count,
-                ATTR_LAST_ERROR: self._last_error,
+                ATTR_API_PROVIDER: self._config_entry.data.get(CONF_API_PROVIDER, "Unknown"),
+                ATTR_TOTAL_ERRORS: metrics.get("total_errors", 0),
                 "instance_name": self._instance_name,
                 "normalized_name": self._normalized_name,
-                ATTR_SYSTEM_PROMPT: data.get("system_prompt"),
+                ATTR_SYSTEM_PROMPT: (data.get("system_prompt", "")[:_ATTR_PROMPT_LIMIT]
+                                   if data.get("system_prompt") else None),
                 ATTR_IS_PROCESSING: data.get("is_processing", False),
                 ATTR_IS_RATE_LIMITED: data.get("is_rate_limited", False),
                 ATTR_IS_MAINTENANCE: data.get("is_maintenance", False),
                 ATTR_ENDPOINT_STATUS: data.get("endpoint_status", "unknown"),
-                ATTR_UPTIME: data.get("uptime", 0),
+                ATTR_UPTIME: round(data.get("uptime", 0), 2),
                 ATTR_HISTORY_SIZE: data.get("history_size", 0),
-                ATTR_CONVERSATION_HISTORY: data.get("conversation_history", []),
             }
 
-            # Add metrics
-            metrics = data.get("metrics", {})
-            if isinstance(metrics, dict):
-                self._metrics = metrics
-                attributes.update(
+            # Conversation history preview (compact: last 3, truncated to 256 chars).
+            # Full history is available via ha_text_ai.get_history service.
+            conversation_history = data.get("conversation_history", [])
+            if conversation_history:
+                preview = conversation_history[-3:]
+                attributes["conversation_history"] = [
                     {
-                        METRIC_TOTAL_TOKENS: metrics.get("total_tokens", 0),
-                        METRIC_PROMPT_TOKENS: metrics.get("prompt_tokens", 0),
-                        METRIC_COMPLETION_TOKENS: metrics.get("completion_tokens", 0),
-                        METRIC_SUCCESSFUL_REQUESTS: metrics.get(
-                            "successful_requests", 0
-                        ),
-                        METRIC_FAILED_REQUESTS: metrics.get("failed_requests", 0),
-                        METRIC_AVERAGE_LATENCY: metrics.get("average_latency", 0),
-                        METRIC_MAX_LATENCY: metrics.get("max_latency", 0),
-                        METRIC_MIN_LATENCY: metrics.get("min_latency", float("inf")),
+                        "timestamp": entry["timestamp"],
+                        "question": entry["question"][:256],
+                        "response": entry["response"][:256],
                     }
-                )
-
-            # Add last response
-            last_response = data.get("last_response", {})
-            if isinstance(last_response, dict):
-                self._last_response = last_response
-                attributes.update(
-                    {
-                        ATTR_RESPONSE: last_response.get("response", ""),
-                        ATTR_QUESTION: last_response.get("question", ""),
-                        "last_model": last_response.get("model", ""),
-                        "last_timestamp": last_response.get("timestamp", ""),
-                        "last_error": last_response.get("error"),
-                    }
-                )
-
-            # Add performance metrics if available
-            if ATTR_PERFORMANCE_METRICS in data:
-                attributes[ATTR_PERFORMANCE_METRICS] = data[
-                    ATTR_PERFORMANCE_METRICS
+                    for entry in preview
                 ]
 
-            # Add API version if available
-            if ATTR_API_VERSION in data:
-                attributes[ATTR_API_VERSION] = data[ATTR_API_VERSION]
+            # Metrics
+            if isinstance(metrics, dict):
+                attributes.update({
+                    METRIC_TOTAL_TOKENS: metrics.get("total_tokens", 0),
+                    METRIC_PROMPT_TOKENS: metrics.get("prompt_tokens", 0),
+                    METRIC_COMPLETION_TOKENS: metrics.get("completion_tokens", 0),
+                    METRIC_SUCCESSFUL_REQUESTS: metrics.get("successful_requests", 0),
+                    METRIC_FAILED_REQUESTS: metrics.get("failed_requests", 0),
+                    METRIC_AVERAGE_LATENCY: round(metrics.get("average_latency", 0), 2),
+                    METRIC_MAX_LATENCY: round(metrics.get("max_latency", 0), 2),
+                    METRIC_MIN_LATENCY: metrics.get("min_latency", 0) or None,
+                })
+
+            # Last response handling
+            last_response = data.get("last_response", {})
+            if isinstance(last_response, dict):
+                attributes.update({
+                    ATTR_RESPONSE: last_response.get("response", "")[:_ATTR_TEXT_LIMIT],
+                    ATTR_QUESTION: last_response.get("question", "")[:_ATTR_TEXT_LIMIT],
+                    "last_model": last_response.get("model", ""),
+                    "last_timestamp": last_response.get("timestamp", ""),
+                    "last_error": (last_response.get("error", "")[:_ATTR_TEXT_LIMIT]
+                                 if last_response.get("error") else None),
+                })
 
             return self._sanitize_attributes(attributes)
 
@@ -278,7 +305,7 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
         """When entity is added to hass."""
         await super().async_added_to_hass()
         self._handle_coordinator_update()
-        _LOGGER.debug(f"Entity {self.entity_id} added to Home Assistant")
+        _LOGGER.debug("Entity %s added to Home Assistant", self.entity_id)
 
     def _handle_coordinator_update(self) -> None:
         """Handle updated data from the coordinator."""
@@ -286,12 +313,18 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
             data = self.coordinator.data
             if not self.coordinator.last_update_success or not data:
                 self._current_state = STATE_DISCONNECTED
-                _LOGGER.warning(f"No data available for {self.entity_id}")
+                _LOGGER.warning("No data available for %s", self.entity_id)
                 self.async_write_ha_state()
                 return
 
             self._is_processing = data.get("is_processing", False)
 
+            # Update metrics
+            metrics = data.get("metrics", {})
+            if isinstance(metrics, dict):
+                self._metrics.update(metrics)
+                _LOGGER.debug("Updated metrics for %s: %s", self.entity_id, self._metrics)
+
             # Update conversation history and system prompt
             self._conversation_history = data.get("conversation_history", [])
             self._system_prompt = data.get("system_prompt")
@@ -314,8 +347,8 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
             self._last_update = dt_util.utcnow()
 
             _LOGGER.debug(
-                f"Updated {self.entity_id} state to: {self._current_state} "
-                f"(available: {self.available})"
+                "Updated %s state to: %s (available: %s)",
+                self.entity_id, self._current_state, self.available,
             )
 
         except Exception as err:

custom_components/ha_text_ai/services.yaml

@@ -3,6 +3,7 @@ ask_question:
   description: >-
     Send a question to the AI model and receive a detailed response.
     The response will be stored in the conversation history and can be retrieved later.
+    This service now returns response data directly, eliminating the need to read from sensors.
   fields:
     instance:
       name: Instance
@@ -63,16 +64,34 @@ ask_question:
 
     max_tokens:
       name: Max Tokens
-      description: Maximum length of the response (1-4096 tokens)
+      description: Maximum length of the response (tokens)
       required: false
       default: 1000
       selector:
         number:
           min: 1
-          max: 4096
+          max: 100000
           step: 1
           mode: box
 
+    structured_output:
+      name: Structured Output
+      description: Enable JSON structured output mode. When enabled, the AI will respond with valid JSON matching the provided schema.
+      required: false
+      default: false
+      selector:
+        boolean: {}
+
+    json_schema:
+      name: JSON Schema
+      description: >-
+        JSON Schema defining the structure of the expected response.
+        Required when structured_output is enabled.
+      required: false
+      selector:
+        text:
+          multiline: true
+
 clear_history:
   name: Clear History
   description: >-
@@ -134,7 +153,7 @@ get_history:
       required: false
       default: false
       selector:
-        boolean:
+        boolean: {}
 
     sort_order:
       name: Sort Order

custom_components/ha_text_ai/strings.json

@@ -0,0 +1,329 @@
+{
+  "config": {
+    "step": {
+      "provider": {
+        "title": "Provider Settings",
+        "description": "Provide connection details for your chosen AI provider.",
+        "data": {
+          "name": "Instance name (e.g., 'GPT Assistant', 'Claude Helper')",
+          "api_key": "API key for authentication",
+          "model": "AI model to use",
+          "api_endpoint": "Custom API endpoint URL (optional)",
+          "temperature": "Response creativity (0-2, lower = more focused)",
+          "max_tokens": "Maximum response length (1-100000 tokens)",
+          "request_interval": "Minimum time between requests (0.1-60 seconds)",
+          "api_timeout": "API request timeout in seconds (5-600)",
+          "context_messages": "Number of context messages to retain (1-20)",
+          "max_history_size": "Maximum conversation history size (1-100)",
+          "allow_local_network": "Allow local network endpoints (for self-hosted proxies)"
+        }
+      },
+      "user": {
+        "title": "Configure HA Text AI Instance",
+        "description": "Set up a new AI assistant instance with your selected provider.",
+        "data": {
+          "name": "Instance name (e.g., 'GPT Assistant', 'Claude Helper')",
+          "api_key": "API key for authentication",
+          "model": "AI model to use",
+          "temperature": "Response creativity (0-2, lower = more focused)",
+          "max_tokens": "Maximum response length (1-100000 tokens)",
+          "api_endpoint": "Custom API endpoint URL (optional)",
+          "api_provider": "API Provider",
+          "request_interval": "Minimum time between requests (0.1-60 seconds)",
+          "api_timeout": "API request timeout in seconds (5-600)",
+          "context_messages": "Number of context messages to retain (1-20)",
+          "max_history_size": "Maximum conversation history size (1-100)",
+          "allow_local_network": "Allow local network endpoints (for self-hosted proxies)"
+        }
+      }
+    },
+    "error": {
+      "history_storage_error": "Failed to initialize history storage. Check permissions.",
+      "history_rotation_error": "Error during history file rotation.",
+      "history_file_access_error": "Cannot access history storage directory.",
+      "name_exists": "An instance with this name already exists",
+      "invalid_name": "Invalid instance name",
+      "invalid_auth": "Authentication failed - check your API key",
+      "api_key_required": "API key is required when changing provider or endpoint",
+      "invalid_api_key": "Invalid API key - please verify your credentials",
+      "cannot_connect": "Failed to connect to API service",
+      "invalid_model": "Selected model is not available",
+      "rate_limit": "Rate limit exceeded",
+      "context_length": "Context length exceeded",
+      "rate_limit_exceeded": "API rate limit exceeded",
+      "maintenance": "Service is under maintenance",
+      "invalid_response": "Invalid API response received",
+      "api_error": "API service error occurred",
+      "timeout": "Request timed out",
+      "invalid_instance": "Invalid instance specified",
+      "unknown": "Unexpected error occurred",
+      "empty": "Name cannot be empty",
+      "name_too_long": "Name must be 50 characters or less"
+    },
+    "abort": {
+      "already_configured": "Instance already configured"
+    }
+  },
+  "options": {
+    "step": {
+      "init": {
+        "title": "Select Provider",
+        "description": "Choose the AI provider for this instance. The integration will reload after saving changes.",
+        "data": {
+          "api_provider": "API Provider"
+        }
+      },
+      "settings": {
+        "title": "Connection & Model Settings",
+        "description": "Configure API credentials and model parameters. Changes will take effect after the integration reloads.",
+        "data": {
+          "api_key": "API Key",
+          "api_endpoint": "API Endpoint URL",
+          "model": "AI model",
+          "temperature": "Response creativity (0-2)",
+          "max_tokens": "Maximum response length (1-100000)",
+          "request_interval": "Minimum request interval (0.1-60 seconds)",
+          "api_timeout": "API request timeout in seconds (5-600)",
+          "context_messages": "Number of previous messages to include in context (1-20)",
+          "max_history_size": "Maximum conversation history size (1-100)",
+          "allow_local_network": "Allow local network endpoints (for self-hosted proxies)"
+        }
+      }
+    }
+  },
+  "selector": {
+    "api_provider": {
+      "options": {
+        "openai": "OpenAI (compatible)",
+        "anthropic": "Anthropic (compatible)",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
+      }
+    }
+  },
+  "services": {
+    "ask_question": {
+      "name": "Ask Question (HA Text AI)",
+      "description": "Send a question to the AI model and receive a detailed response. This service now returns response data directly, eliminating the need for separate text sensors and the 255-character limitation. The response will also be stored in the conversation history.",
+      "fields": {
+        "instance": {
+          "name": "Instance",
+          "description": "Name of the HA Text AI instance to use"
+        },
+        "question": {
+          "name": "Question",
+          "description": "Your question or prompt for the AI assistant"
+        },
+        "context_messages": {
+          "name": "Context Messages",
+          "description": "Number of previous messages to include in context (1-20)"
+        },
+        "system_prompt": {
+          "name": "System Prompt",
+          "description": "Optional system prompt to set context for this specific question"
+        },
+        "model": {
+          "name": "Model",
+          "description": "Select AI model to use (optional, overrides default setting)"
+        },
+        "temperature": {
+          "name": "Temperature",
+          "description": "Controls response creativity (0.0-2.0)"
+        },
+        "max_tokens": {
+          "name": "Max Tokens",
+          "description": "Maximum length of the response (1-100000 tokens)"
+        },
+        "structured_output": {
+          "name": "Structured Output",
+          "description": "Enable JSON structured output mode. When enabled, the AI will respond with valid JSON matching the provided schema."
+        },
+        "json_schema": {
+          "name": "JSON Schema",
+          "description": "JSON Schema defining the structure of the expected response. Required when structured_output is enabled."
+        }
+      }
+    },
+    "clear_history": {
+      "name": "Clear History",
+      "description": "Delete all stored questions and responses from the conversation history",
+      "fields": {
+        "instance": {
+          "name": "Instance",
+          "description": "Name of the HA Text AI instance to clear history for"
+        }
+      }
+    },
+    "get_history": {
+      "name": "Get History",
+      "description": "Retrieve conversation history with optional filtering and sorting",
+      "fields": {
+        "instance": {
+          "name": "Instance",
+          "description": "Name of the HA Text AI instance to get history from"
+        },
+        "limit": {
+          "name": "Limit",
+          "description": "Number of conversations to return (1-100)"
+        },
+        "filter_model": {
+          "name": "Filter Model",
+          "description": "Filter conversations by specific AI model"
+        },
+        "start_date": {
+          "name": "Start Date",
+          "description": "Filter conversations starting from this date/time"
+        },
+        "include_metadata": {
+          "name": "Include Metadata",
+          "description": "Include additional information like tokens used, response time, etc."
+        },
+        "sort_order": {
+          "name": "Sort Order",
+          "description": "Sort order for results (newest or oldest first)"
+        }
+      }
+    },
+    "set_system_prompt": {
+      "name": "Set System Prompt",
+      "description": "Set default system behavior instructions for all future conversations",
+      "fields": {
+        "instance": {
+          "name": "Instance",
+          "description": "Name of the HA Text AI instance to set system prompt for"
+        },
+        "prompt": {
+          "name": "System Prompt",
+          "description": "Instructions that define how the AI should behave and respond"
+        }
+      }
+    }
+  },
+  "entity": {
+    "sensor": {
+      "ha_text_ai": {
+        "name": "{name}",
+        "state": {
+          "ready": "Ready",
+          "processing": "Processing",
+          "error": "Error",
+          "disconnected": "Disconnected",
+          "rate_limited": "Rate Limited",
+          "maintenance": "Maintenance",
+          "initializing": "Initializing",
+          "retrying": "Retrying"
+        },
+        "state_attributes": {
+          "question": {
+            "name": "Last Question"
+          },
+          "response": {
+            "name": "Last Response"
+          },
+          "model": {
+            "name": "Current Model"
+          },
+          "temperature": {
+            "name": "Temperature"
+          },
+          "max_tokens": {
+            "name": "Max Tokens"
+          },
+          "system_prompt": {
+            "name": "System Prompt"
+          },
+          "response_time": {
+            "name": "Last Response Time"
+          },
+          "total_responses": {
+            "name": "Total Responses"
+          },
+          "error_count": {
+            "name": "Error Count"
+          },
+          "last_error": {
+            "name": "Last Error"
+          },
+          "api_status": {
+            "name": "API Status"
+          },
+          "tokens_used": {
+            "name": "Total Tokens Used"
+          },
+          "average_response_time": {
+            "name": "Average Response Time"
+          },
+          "last_request_time": {
+            "name": "Last Request Time"
+          },
+          "is_processing": {
+            "name": "Processing Status"
+          },
+          "is_rate_limited": {
+            "name": "Rate Limited Status"
+          },
+          "is_maintenance": {
+            "name": "Maintenance Status"
+          },
+          "api_version": {
+            "name": "API Version"
+          },
+          "endpoint_status": {
+            "name": "Endpoint Status"
+          },
+          "performance_metrics": {
+            "name": "Performance Metrics"
+          },
+          "history_size": {
+            "name": "History Size"
+          },
+          "uptime": {
+            "name": "Uptime"
+          },
+          "total_tokens": {
+            "name": "Total Tokens"
+          },
+          "prompt_tokens": {
+            "name": "Prompt Tokens"
+          },
+          "completion_tokens": {
+            "name": "Completion Tokens"
+          },
+          "successful_requests": {
+            "name": "Successful Requests"
+          },
+          "failed_requests": {
+            "name": "Failed Requests"
+          },
+          "average_latency": {
+            "name": "Average Latency"
+          },
+          "max_latency": {
+            "name": "Maximum Latency"
+          },
+          "min_latency": {
+            "name": "Minimum Latency"
+          },
+          "last_model": {
+            "name": "Last Used Model"
+          },
+          "last_timestamp": {
+            "name": "Last Response Time"
+          },
+          "instance_name": {
+            "name": "Instance Name"
+          },
+          "normalized_name": {
+            "name": "Normalized Name"
+          },
+          "last_error": {
+            "name": "Last Error"
+          },
+          "conversation_history": {
+            "name": "Conversation History"
+          }
+        }
+      }
+    }
+  }
+}

custom_components/ha_text_ai/translations/de.json

@@ -2,71 +2,109 @@
   "config": {
     "step": {
       "provider": {
-        "title": "KI-Anbieter auswählen",
-        "description": "Wählen Sie den KI-Dienstanbieter für diese Instanz aus.",
+        "title": "Anbieter-Einstellungen",
+        "description": "Geben Sie die Verbindungsdetails für Ihren gewählten AI-Anbieter an.",
         "data": {
-          "api_provider": "API-Anbieter",
-          "context_messages": "Anzahl der zu behaltenden Kontextnachrichten (1-20)"
+          "name": "Instanzname (z. B. 'GPT Assistant', 'Claude Helper')",
+          "api_key": "API-Schlüssel zur Authentifizierung",
+          "model": "Zu verwendendes AI-Modell",
+          "api_endpoint": "Benutzerdefinierte API-Endpunkt-URL (optional)",
+          "temperature": "Kreativität der Antwort (0-2, niedriger = fokussierter)",
+          "max_tokens": "Maximale Länge der Antwort (1-100000 Token)",
+          "request_interval": "Minimale Zeit zwischen Anfragen (0,1-60 Sekunden)",
+          "api_timeout": "API-Anfrage Timeout in Sekunden (5-600)",
+          "context_messages": "Anzahl der zu behaltenden Kontextnachrichten (1-20)",
+          "max_history_size": "Maximale Größe des Gesprächsverlaufs (1-100)",
+          "allow_local_network": "Lokale Netzwerkendpunkte erlauben (für selbst gehostete Proxys)"
         }
       },
       "user": {
-        "title": "HA Text AI-Instanz konfigurieren",
-        "description": "Richten Sie eine neue KI-Assistenteninstanz mit Ihrem ausgewählten Anbieter ein.",
+        "title": "HA Text AI Instanz konfigurieren",
+        "description": "Richten Sie eine neue AI-Assistenteninstanz mit Ihrem ausgewählten Anbieter ein.",
         "data": {
-          "name": "Instanzname (z. B. 'GPT-Assistent', 'Claude-Helfer')",
+          "name": "Instanzname (z. B. 'GPT Assistant', 'Claude Helper')",
           "api_key": "API-Schlüssel zur Authentifizierung",
-          "model": "Zu verwendendes KI-Modell",
-          "temperature": "Antwortkreativität (0-2, niedriger = fokussierter)",
-          "max_tokens": "Maximale Antwortlänge (1-4096 Token)",
+          "model": "Zu verwendendes AI-Modell",
+          "temperature": "Kreativität der Antwort (0-2, niedriger = fokussierter)",
+          "max_tokens": "Maximale Länge der Antwort (1-100000 Token)",
           "api_endpoint": "Benutzerdefinierte API-Endpunkt-URL (optional)",
           "api_provider": "API-Anbieter",
-          "request_interval": "Mindestzeit zwischen Anfragen (0,1-60 Sekunden)",
+          "request_interval": "Minimale Zeit zwischen Anfragen (0,1-60 Sekunden)",
+          "api_timeout": "API-Anfrage Timeout in Sekunden (5-600)",
           "context_messages": "Anzahl der zu behaltenden Kontextnachrichten (1-20)",
-          "max_history_size": "Maximale Größe des Konversationsverlaufs (1-100)"
+          "max_history_size": "Maximale Größe des Gesprächsverlaufs (1-100)",
+          "allow_local_network": "Lokale Netzwerkendpunkte erlauben (für selbst gehostete Proxys)"
         }
       }
     },
     "error": {
+      "history_storage_error": "Fehler beim Initialisieren des Verlaufspeichers. Überprüfen Sie die Berechtigungen.",
+      "history_rotation_error": "Fehler beim Drehen der Verlaufsdatei.",
+      "history_file_access_error": "Zugriff auf das Verzeichnis für den Verlaufsspeicher nicht möglich.",
       "name_exists": "Eine Instanz mit diesem Namen existiert bereits",
       "invalid_name": "Ungültiger Instanzname",
       "invalid_auth": "Authentifizierung fehlgeschlagen - überprüfen Sie Ihren API-Schlüssel",
-      "invalid_api_key": "Ungültiger API-Schlüssel - überprüfen Sie Ihre Anmeldedaten",
+      "invalid_api_key": "Ungültiger API-Schlüssel - bitte überprüfen Sie Ihre Anmeldeinformationen",
       "cannot_connect": "Verbindung zum API-Dienst fehlgeschlagen",
-      "invalid_model": "Das ausgewählte Modell ist nicht verfügbar",
-      "rate_limit": "Ratenlimit überschritten",
+      "invalid_model": "Ausgewähltes Modell ist nicht verfügbar",
+      "rate_limit": "Rate-Limit überschritten",
       "context_length": "Kontextlänge überschritten",
-      "rate_limit_exceeded": "API-Ratenlimit überschritten",
-      "maintenance": "Dienst befindet sich in der Wartung",
-      "invalid_response": "Ungültige API-Antwort empfangen",
-      "api_error": "Fehler im API-Dienst aufgetreten",
-      "timeout": "Anfrage ist abgelaufen",
+      "rate_limit_exceeded": "API-Rate-Limit überschritten",
+      "maintenance": "Dienst ist in Wartung",
+      "invalid_response": "Ungültige API-Antwort erhalten",
+      "api_error": "Ein Fehler im API-Dienst ist aufgetreten",
+      "timeout": "Zeitüberschreitung bei der Anfrage",
       "invalid_instance": "Ungültige Instanz angegeben",
-      "unknown": "Es ist ein unerwarteter Fehler aufgetreten",
+      "unknown": "Unerwarteter Fehler aufgetreten",
       "empty": "Name darf nicht leer sein",
       "invalid_characters": "Name darf nur Buchstaben, Zahlen, Leerzeichen, Unterstriche und Bindestriche enthalten",
-      "name_too_long": "Name darf maximal 50 Zeichen lang sein"
+      "name_too_long": "Name darf höchstens 50 Zeichen lang sein"
+    },
+    "abort": {
+      "already_configured": "Instanz bereits konfiguriert"
     }
   },
   "options": {
     "step": {
       "init": {
-        "title": "Instanzeinstellungen aktualisieren",
-        "description": "Ändern Sie die Einstellungen für diese KI-Assistenteninstanz.",
+        "title": "Anbieter auswählen",
+        "description": "Wählen Sie den AI-Anbieter für diese Instanz. Die Integration wird nach dem Speichern der Änderungen neu geladen.",
         "data": {
-          "model": "KI-Modell",
-          "temperature": "Antwortkreativität (0-2)",
-          "max_tokens": "Maximale Antwortlänge (1-4096)",
-          "request_interval": "Mindestzeitraum zwischen Anfragen (0,1-60 Sekunden)",
-          "context_messages": "Anzahl der vorherigen Nachrichten, die im Kontext enthalten sein sollen (1-20)",
-          "max_history_size": "Maximale Größe des Konversationsverlaufs (1-100)"
+          "api_provider": "API-Anbieter"
         }
+      },
+      "settings": {
+        "title": "Verbindungs- und Modelleinstellungen",
+        "description": "Konfigurieren Sie API-Anmeldeinformationen und Modellparameter. Änderungen werden nach dem Neuladen der Integration wirksam.",
+        "data": {
+          "api_key": "API-Schlüssel",
+          "api_endpoint": "API-Endpunkt-URL",
+          "model": "AI-Modell",
+          "temperature": "Kreativität der Antwort (0-2)",
+          "max_tokens": "Maximale Länge der Antwort (1-100000)",
+          "request_interval": "Minimales Anfrageintervall (0,1-60 Sekunden)",
+          "api_timeout": "API-Anfrage Timeout in Sekunden (5-600)",
+          "context_messages": "Anzahl der vorherigen Nachrichten, die im Kontext enthalten sein sollen (1-20)",
+          "max_history_size": "Maximale Größe des Gesprächsverlaufs (1-100)",
+          "allow_local_network": "Lokale Netzwerkendpunkte erlauben (für selbst gehostete Proxys)"
+        }
+      }
+    }
+  },
+  "selector": {
+    "api_provider": {
+      "options": {
+        "openai": "OpenAI (compatible)",
+        "anthropic": "Anthropic (compatible)",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
       }
     }
   },
   "services": {
     "ask_question": {
       "name": "Frage stellen (HA Text AI)",
-      "description": "Senden Sie eine Frage an das KI-Modell und erhalten Sie eine detaillierte Antwort. Die Antwort wird im Konversationsverlauf gespeichert und kann später abgerufen werden.",
+      "description": "Stellen Sie eine Frage an das AI-Modell und erhalten Sie eine detaillierte Antwort. Dieser Service gibt jetzt Antwortdaten direkt zurück, wodurch separate Textsensoren und die 255-Zeichen-Begrenzung überflüssig werden. Die Antwort wird auch im Gesprächsverlauf gespeichert.",
       "fields": {
         "instance": {
           "name": "Instanz",
@@ -74,43 +112,51 @@
         },
         "question": {
           "name": "Frage",
-          "description": "Ihre Frage oder Aufforderung an den KI-Assistenten"
+          "description": "Ihre Frage oder Aufforderung für den AI-Assistenten"
         },
         "context_messages": {
           "name": "Kontextnachrichten",
           "description": "Anzahl der vorherigen Nachrichten, die im Kontext enthalten sein sollen (1-20)"
         },
         "system_prompt": {
-          "name": "Systemprompt",
-          "description": "Optionaler Systemprompt, um den Kontext für diese spezielle Frage festzulegen"
+          "name": "Systemaufforderung",
+          "description": "Optionale Systemaufforderung zur Festlegung des Kontexts für diese spezifische Frage"
         },
         "model": {
           "name": "Modell",
-          "description": "Wählen Sie das zu verwendende KI-Modell aus (optional, überschreibt die Standardeinstellung)"
+          "description": "Wählen Sie das zu verwendende AI-Modell (optional, überschreibt die Standardeinstellung)"
         },
         "temperature": {
           "name": "Temperatur",
-          "description": "Steuert die Antwortkreativität (0.0-2.0)"
+          "description": "Steuert die Kreativität der Antwort (0,0-2,0)"
         },
         "max_tokens": {
-          "name": "Max. Token",
-          "description": "Maximale Länge der Antwort (1-4096 Token)"
+          "name": "Max Tokens",
+          "description": "Maximale Länge der Antwort (1-100000 Token)"
+        },
+        "structured_output": {
+          "name": "Strukturierte Ausgabe",
+          "description": "JSON-Strukturausgabemodus aktivieren. Bei Aktivierung antwortet die KI mit gültigem JSON, das dem angegebenen Schema entspricht."
+        },
+        "json_schema": {
+          "name": "JSON-Schema",
+          "description": "JSON-Schema, das die Struktur der erwarteten Antwort definiert. Erforderlich wenn structured_output aktiviert ist."
         }
       }
     },
     "clear_history": {
       "name": "Verlauf löschen",
-      "description": "Alle gespeicherten Fragen und Antworten aus dem Konversationsverlauf löschen",
+      "description": "Löschen Sie alle gespeicherten Fragen und Antworten aus dem Gesprächsverlauf",
       "fields": {
         "instance": {
           "name": "Instanz",
-          "description": "Name der HA Text AI-Instanz, deren Verlauf gelöscht werden soll"
+          "description": "Name der HA Text AI-Instanz, für die der Verlauf gelöscht werden soll"
         }
       }
     },
     "get_history": {
       "name": "Verlauf abrufen",
-      "description": "Konversationsverlauf mit optionalem Filtern und Sortieren abrufen",
+      "description": "Rufen Sie den Gesprächsverlauf mit optionaler Filterung und Sortierung ab",
       "fields": {
         "instance": {
           "name": "Instanz",
@@ -118,19 +164,19 @@
         },
         "limit": {
           "name": "Limit",
-          "description": "Anzahl der zurückzugebenden Konversationen (1-100)"
+          "description": "Anzahl der zurückzugebenden Gespräche (1-100)"
         },
         "filter_model": {
           "name": "Modell filtern",
-          "description": "Konversationen nach einem bestimmten KI-Modell filtern"
+          "description": "Gespräche nach spezifischem AI-Modell filtern"
         },
         "start_date": {
           "name": "Startdatum",
-          "description": "Konversationen ab diesem Datum/Uhrzeit filtern"
+          "description": "Gespräche ab diesem Datum/Zeit filtern"
         },
         "include_metadata": {
-          "name": "Metadaten einschließen",
-          "description": "Zusätzliche Informationen wie verwendete Token, Antwortzeit usw. einschließen"
+          "name": "Metadaten einbeziehen",
+          "description": "Zusätzliche Informationen wie verwendete Tokens, Antwortzeit usw. einbeziehen"
         },
         "sort_order": {
           "name": "Sortierreihenfolge",
@@ -139,16 +185,16 @@
       }
     },
     "set_system_prompt": {
-      "name": "Systemprompt festlegen",
-      "description": "Legen Sie Standardanweisungen für das Systemverhalten für alle zukünftigen Konversationen fest",
+      "name": "Systemaufforderung festlegen",
+      "description": "Standardverhaltensanweisungen für alle zukünftigen Gespräche festlegen",
       "fields": {
         "instance": {
           "name": "Instanz",
-          "description": "Name der HA Text AI-Instanz, für die der Systemprompt festgelegt werden soll"
+          "description": "Name der HA Text AI-Instanz, für die die Systemaufforderung festgelegt werden soll"
         },
         "prompt": {
-          "name": "Systemprompt",
-          "description": "Anweisungen, die definieren, wie sich die KI verhalten und antworten soll"
+          "name": "Systemaufforderung",
+          "description": "Anweisungen, die definieren, wie die AI sich verhalten und antworten soll"
         }
       }
     }
@@ -162,11 +208,11 @@
           "processing": "Verarbeitung",
           "error": "Fehler",
           "disconnected": "Getrennt",
-          "rate_limited": "Ratenlimit",
+          "rate_limited": "Rate limitiert",
           "maintenance": "Wartung",
           "initializing": "Initialisierung",
           "retrying": "Wiederholen",
-          "queued": "Warteschlange"
+          "queued": "In der Warteschlange"
         },
         "state_attributes": {
           "question": {
@@ -182,16 +228,16 @@
             "name": "Temperatur"
           },
           "max_tokens": {
-            "name": "Max. Token"
+            "name": "Max Tokens"
           },
           "system_prompt": {
-            "name": "Systemprompt"
+            "name": "Systemaufforderung"
           },
           "response_time": {
             "name": "Letzte Antwortzeit"
           },
           "total_responses": {
-            "name": "Gesamtzahl der Antworten"
+            "name": "Gesamtantworten"
           },
           "error_count": {
             "name": "Fehleranzahl"
@@ -203,19 +249,19 @@
             "name": "API-Status"
           },
           "tokens_used": {
-            "name": "Verwendete Token insgesamt"
+            "name": "Gesamte verwendete Tokens"
           },
           "average_response_time": {
             "name": "Durchschnittliche Antwortzeit"
           },
           "last_request_time": {
-            "name": "Zeitpunkt der letzten Anfrage"
+            "name": "Letzte Anfragezeit"
           },
           "is_processing": {
             "name": "Verarbeitungsstatus"
           },
           "is_rate_limited": {
-            "name": "Ratenlimit-Status"
+            "name": "Rate-limitiert Status"
           },
           "is_maintenance": {
             "name": "Wartungsstatus"
@@ -224,25 +270,25 @@
             "name": "API-Version"
           },
           "endpoint_status": {
-            "name": "Endpunkt-Status"
+            "name": "Endpunktstatus"
           },
           "performance_metrics": {
-            "name": "Leistungsmetriken"
+            "name": "Leistungskennzahlen"
           },
           "history_size": {
-            "name": "Verlaufsgröße"
+            "name": "Größe des Verlaufs"
           },
           "uptime": {
             "name": "Betriebszeit"
           },
           "total_tokens": {
-            "name": "Gesamtzahl der Token"
+            "name": "Gesamte Tokens"
           },
           "prompt_tokens": {
-            "name": "Prompt-Token"
+            "name": "Eingabe Tokens"
           },
           "completion_tokens": {
-            "name": "Completion-Token"
+            "name": "Vervollständigungs Tokens"
           },
           "successful_requests": {
             "name": "Erfolgreiche Anfragen"

custom_components/ha_text_ai/translations/en.json

@@ -2,11 +2,20 @@
   "config": {
     "step": {
       "provider": {
-        "title": "Select AI Provider",
-        "description": "Choose which AI service provider to use for this instance.",
+        "title": "Provider Settings",
+        "description": "Provide connection details for your chosen AI provider.",
         "data": {
-          "api_provider": "API Provider",
-          "context_messages": "Number of context messages to retain (1-20)"
+          "name": "Instance name (e.g., 'GPT Assistant', 'Claude Helper')",
+          "api_key": "API key for authentication",
+          "model": "AI model to use",
+          "api_endpoint": "Custom API endpoint URL (optional)",
+          "temperature": "Response creativity (0-2, lower = more focused)",
+          "max_tokens": "Maximum response length (1-100000 tokens)",
+          "request_interval": "Minimum time between requests (0.1-60 seconds)",
+          "api_timeout": "API request timeout in seconds (5-600)",
+          "context_messages": "Number of context messages to retain (1-20)",
+          "max_history_size": "Maximum conversation history size (1-100)",
+          "allow_local_network": "Allow local network endpoints (for self-hosted proxies)"
         }
       },
       "user": {
@@ -17,19 +26,25 @@
           "api_key": "API key for authentication",
           "model": "AI model to use",
           "temperature": "Response creativity (0-2, lower = more focused)",
-          "max_tokens": "Maximum response length (1-4096 tokens)",
+          "max_tokens": "Maximum response length (1-100000 tokens)",
           "api_endpoint": "Custom API endpoint URL (optional)",
           "api_provider": "API Provider",
           "request_interval": "Minimum time between requests (0.1-60 seconds)",
+          "api_timeout": "API request timeout in seconds (5-600)",
           "context_messages": "Number of context messages to retain (1-20)",
-          "max_history_size": "Maximum conversation history size (1-100)"
+          "max_history_size": "Maximum conversation history size (1-100)",
+          "allow_local_network": "Allow local network endpoints (for self-hosted proxies)"
         }
       }
     },
     "error": {
+      "history_storage_error": "Failed to initialize history storage. Check permissions.",
+      "history_rotation_error": "Error during history file rotation.",
+      "history_file_access_error": "Cannot access history storage directory.",
       "name_exists": "An instance with this name already exists",
       "invalid_name": "Invalid instance name",
       "invalid_auth": "Authentication failed - check your API key",
+      "api_key_required": "API key is required when changing provider or endpoint",
       "invalid_api_key": "Invalid API key - please verify your credentials",
       "cannot_connect": "Failed to connect to API service",
       "invalid_model": "Selected model is not available",
@@ -43,30 +58,53 @@
       "invalid_instance": "Invalid instance specified",
       "unknown": "Unexpected error occurred",
       "empty": "Name cannot be empty",
-      "invalid_characters": "Name can only contain letters, numbers, spaces, underscores and hyphens",
       "name_too_long": "Name must be 50 characters or less"
+    },
+    "abort": {
+      "already_configured": "Instance already configured"
     }
   },
   "options": {
     "step": {
       "init": {
-        "title": "Update Instance Settings",
-        "description": "Modify settings for this AI assistant instance.",
+        "title": "Select Provider",
+        "description": "Choose the AI provider for this instance. The integration will reload after saving changes.",
         "data": {
+          "api_provider": "API Provider"
+        }
+      },
+      "settings": {
+        "title": "Connection & Model Settings",
+        "description": "Configure API credentials and model parameters. Changes will take effect after the integration reloads.",
+        "data": {
+          "api_key": "API Key",
+          "api_endpoint": "API Endpoint URL",
           "model": "AI model",
           "temperature": "Response creativity (0-2)",
-          "max_tokens": "Maximum response length (1-4096)",
+          "max_tokens": "Maximum response length (1-100000)",
           "request_interval": "Minimum request interval (0.1-60 seconds)",
+          "api_timeout": "API request timeout in seconds (5-600)",
           "context_messages": "Number of previous messages to include in context (1-20)",
-          "max_history_size": "Maximum conversation history size (1-100)"
+          "max_history_size": "Maximum conversation history size (1-100)",
+          "allow_local_network": "Allow local network endpoints (for self-hosted proxies)"
         }
       }
     }
   },
+  "selector": {
+    "api_provider": {
+      "options": {
+        "openai": "OpenAI (compatible)",
+        "anthropic": "Anthropic (compatible)",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
+      }
+    }
+  },
   "services": {
     "ask_question": {
       "name": "Ask Question (HA Text AI)",
-      "description": "Send a question to the AI model and receive a detailed response. The response will be stored in the conversation history and can be retrieved later.",
+      "description": "Send a question to the AI model and receive a detailed response. This service now returns response data directly, eliminating the need for separate text sensors and the 255-character limitation. The response will also be stored in the conversation history.",
       "fields": {
         "instance": {
           "name": "Instance",
@@ -94,7 +132,15 @@
         },
         "max_tokens": {
           "name": "Max Tokens",
-          "description": "Maximum length of the response (1-4096 tokens)"
+          "description": "Maximum length of the response (1-100000 tokens)"
+        },
+        "structured_output": {
+          "name": "Structured Output",
+          "description": "Enable JSON structured output mode. When enabled, the AI will respond with valid JSON matching the provided schema."
+        },
+        "json_schema": {
+          "name": "JSON Schema",
+          "description": "JSON Schema defining the structure of the expected response. Required when structured_output is enabled."
         }
       }
     },
@@ -165,8 +211,7 @@
           "rate_limited": "Rate Limited",
           "maintenance": "Maintenance",
           "initializing": "Initializing",
-          "retrying": "Retrying",
-          "queued": "Queued"
+          "retrying": "Retrying"
         },
         "state_attributes": {
           "question": {
@@ -258,6 +303,24 @@
           },
           "min_latency": {
             "name": "Minimum Latency"
+          },
+          "last_model": {
+            "name": "Last Used Model"
+          },
+          "last_timestamp": {
+            "name": "Last Response Time"
+          },
+          "instance_name": {
+            "name": "Instance Name"
+          },
+          "normalized_name": {
+            "name": "Normalized Name"
+          },
+          "last_error": {
+            "name": "Last Error"
+          },
+          "conversation_history": {
+            "name": "Conversation History"
           }
         }
       }

custom_components/ha_text_ai/translations/es.json

@@ -0,0 +1,312 @@
+{
+  "config": {
+    "step": {
+      "provider": {
+        "title": "Configuración del proveedor",
+        "description": "Proporciona los detalles de conexión para tu proveedor de IA elegido.",
+        "data": {
+          "name": "Nombre de la instancia (por ejemplo, 'Asistente GPT', 'Ayudante Claude')",
+          "api_key": "Clave API para autenticación",
+          "model": "Modelo de IA a utilizar",
+          "api_endpoint": "URL del endpoint de API personalizado (opcional)",
+          "temperature": "Creatividad de la respuesta (0-2, menor = más enfocado)",
+          "max_tokens": "Longitud máxima de la respuesta (1-100000 tokens)",
+          "request_interval": "Tiempo mínimo entre solicitudes (0.1-60 segundos)",
+          "api_timeout": "Tiempo de espera de solicitud API en segundos (5-600)",
+          "context_messages": "Número de mensajes de contexto a retener (1-20)",
+          "max_history_size": "Tamaño máximo del historial de conversación (1-100)",
+          "allow_local_network": "Permitir endpoints de red local (para proxies autoalojados)"
+        }
+      },
+      "user": {
+        "title": "Configurar instancia de IA de texto de HA",
+        "description": "Configura una nueva instancia de asistente de IA con tu proveedor seleccionado.",
+        "data": {
+          "name": "Nombre de la instancia (por ejemplo, 'Asistente GPT', 'Ayudante Claude')",
+          "api_key": "Clave API para autenticación",
+          "model": "Modelo de IA a utilizar",
+          "temperature": "Creatividad de la respuesta (0-2, menor = más enfocado)",
+          "max_tokens": "Longitud máxima de la respuesta (1-100000 tokens)",
+          "api_endpoint": "URL del endpoint de API personalizado (opcional)",
+          "api_provider": "Proveedor de API",
+          "request_interval": "Tiempo mínimo entre solicitudes (0.1-60 segundos)",
+          "api_timeout": "Tiempo de espera de solicitud API en segundos (5-600)",
+          "context_messages": "Número de mensajes de contexto a retener (1-20)",
+          "max_history_size": "Tamaño máximo del historial de conversación (1-100)",
+          "allow_local_network": "Permitir endpoints de red local (para proxies autoalojados)"
+        }
+      }
+    },
+    "error": {
+      "history_storage_error": "Error al inicializar el almacenamiento del historial. Verifica los permisos.",
+      "history_rotation_error": "Error durante la rotación del archivo de historial.",
+      "history_file_access_error": "No se puede acceder al directorio de almacenamiento del historial.",
+      "name_exists": "Ya existe una instancia con este nombre",
+      "invalid_name": "Nombre de instancia no válido",
+      "invalid_auth": "La autenticación falló - verifica tu clave API",
+      "invalid_api_key": "Clave API no válida - verifica tus credenciales",
+      "cannot_connect": "Error al conectar con el servicio de API",
+      "invalid_model": "El modelo seleccionado no está disponible",
+      "rate_limit": "Límite de tasa excedido",
+      "context_length": "Longitud del contexto excedida",
+      "rate_limit_exceeded": "Límite de tasa de API excedido",
+      "maintenance": "El servicio está en mantenimiento",
+      "invalid_response": "Respuesta de API no válida recibida",
+      "api_error": "Ocurrió un error en el servicio de API",
+      "timeout": "Se agotó el tiempo de la solicitud",
+      "invalid_instance": "Instancia no válida especificada",
+      "unknown": "Ocurrió un error inesperado",
+      "empty": "El nombre no puede estar vacío",
+      "invalid_characters": "El nombre solo puede contener letras, números, espacios, guiones bajos y guiones",
+      "name_too_long": "El nombre debe tener 50 caracteres o menos"
+    },
+    "abort": {
+      "already_configured": "Instancia ya configurada"
+    }
+  },
+  "options": {
+    "step": {
+      "init": {
+        "title": "Seleccionar proveedor",
+        "description": "Elige el proveedor de IA para esta instancia. La integración se recargará después de guardar los cambios.",
+        "data": {
+          "api_provider": "Proveedor de API"
+        }
+      },
+      "settings": {
+        "title": "Configuración de conexión y modelo",
+        "description": "Configura las credenciales de API y los parámetros del modelo. Los cambios tendrán efecto después de recargar la integración.",
+        "data": {
+          "api_key": "Clave API",
+          "api_endpoint": "URL del endpoint de API",
+          "model": "Modelo de IA",
+          "temperature": "Creatividad de la respuesta (0-2)",
+          "max_tokens": "Longitud máxima de la respuesta (1-100000)",
+          "request_interval": "Intervalo mínimo de solicitud (0.1-60 segundos)",
+          "api_timeout": "Tiempo de espera de solicitud API en segundos (5-600)",
+          "context_messages": "Número de mensajes anteriores a incluir en el contexto (1-20)",
+          "max_history_size": "Tamaño máximo del historial de conversación (1-100)",
+          "allow_local_network": "Permitir endpoints de red local (para proxies autoalojados)"
+        }
+      }
+    }
+  },
+  "selector": {
+    "api_provider": {
+      "options": {
+        "openai": "OpenAI (compatible)",
+        "anthropic": "Anthropic (compatible)",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
+      }
+    }
+  },
+  "services": {
+    "ask_question": {
+      "name": "Hacer Pregunta (HA Text AI)",
+      "description": "Envía una pregunta al modelo de IA y recibe una respuesta detallada. Este servicio ahora devuelve datos de respuesta directamente, eliminando la necesidad de sensores de texto separados y la limitación de 255 caracteres. La respuesta también se almacenará en el historial de conversación.",
+      "fields": {
+        "instance": {
+          "name": "Instancia",
+          "description": "Nombre de la instancia de IA de Texto de HA a utilizar"
+        },
+        "question": {
+          "name": "Pregunta",
+          "description": "Tu pregunta o solicitud para el asistente de IA"
+        },
+        "context_messages": {
+          "name": "Mensajes de Contexto",
+          "description": "Número de mensajes anteriores a incluir en el contexto (1-20)"
+        },
+        "system_prompt": {
+          "name": "Indicaciones del Sistema",
+          "description": "Indicaciones opcionales para establecer contexto para esta pregunta específica"
+        },
+        "model": {
+          "name": "Modelo",
+          "description": "Selecciona el modelo de IA a utilizar (opcional, anula la configuración predeterminada)"
+        },
+        "temperature": {
+          "name": "Temperatura",
+          "description": "Controla la creatividad de la respuesta (0.0-2.0)"
+        },
+        "max_tokens": {
+          "name": "Máx. Tokens",
+          "description": "Longitud máxima de la respuesta (1-100000 tokens)"
+        },
+        "structured_output": {
+          "name": "Salida Estructurada",
+          "description": "Habilitar modo de salida JSON estructurada. Cuando está habilitado, la IA responderá con JSON válido que coincida con el esquema proporcionado."
+        },
+        "json_schema": {
+          "name": "Esquema JSON",
+          "description": "Esquema JSON que define la estructura de la respuesta esperada. Requerido cuando structured_output está habilitado."
+        }
+      }
+    },
+    "clear_history": {
+      "name": "Borrar Historial",
+      "description": "Elimina todas las preguntas y respuestas almacenadas del historial de conversación",
+      "fields": {
+        "instance": {
+          "name": "Instancia",
+          "description": "Nombre de la instancia de IA de Texto de HA para borrar el historial"
+        }
+      }
+    },
+    "get_history": {
+      "name": "Obtener Historial",
+      "description": "Recupera el historial de conversación con filtrado y ordenación opcionales",
+      "fields": {
+        "instance": {
+          "name": "Instancia",
+          "description": "Nombre de la instancia de IA de Texto de HA para obtener historial"
+        },
+        "limit": {
+          "name": "Límite",
+          "description": "Número de conversaciones a devolver (1-100)"
+        },
+        "filter_model": {
+          "name": "Filtrar Modelo",
+          "description": "Filtrar conversaciones por modelo de IA específico"
+        },
+        "start_date": {
+          "name": "Fecha de Inicio",
+          "description": "Filtrar conversaciones a partir de esta fecha/hora"
+        },
+        "include_metadata": {
+          "name": "Incluir Metadatos",
+          "description": "Incluir información adicional como tokens utilizados, tiempo de respuesta, etc."
+        },
+        "sort_order": {
+          "name": "Orden de Clasificación",
+          "description": "Orden de clasificación para los resultados (más recientes o más antiguos primero)"
+        }
+      }
+    },
+    "set_system_prompt": {
+      "name": "Establecer Indicaciones del Sistema",
+      "description": "Establecer instrucciones de comportamiento del sistema predeterminadas para todas las futuras conversaciones",
+      "fields": {
+        "instance": {
+          "name": "Instancia",
+          "description": "Nombre de la instancia de IA de Texto de HA para establecer indicaciones del sistema"
+        },
+        "prompt": {
+          "name": "Indicaciones del Sistema",
+          "description": "Instrucciones que definen cómo debe comportarse y responder la IA"
+        }
+      }
+    }
+  },
+  "entity": {
+    "sensor": {
+      "ha_text_ai": {
+        "name": "{name}",
+        "state": {
+          "ready": "Listo",
+          "processing": "Procesando",
+          "error": "Error",
+          "disconnected": "Desconectado",
+          "rate_limited": "Limitado por tasa",
+          "maintenance": "Mantenimiento",
+          "initializing": "Inicializando",
+          "retrying": "Reintentando",
+          "queued": "En cola"
+        },
+        "state_attributes": {
+          "question": {
+            "name": "Última Pregunta"
+          },
+          "response": {
+            "name": "Última Respuesta"
+          },
+          "model": {
+            "name": "Modelo Actual"
+          },
+          "temperature": {
+            "name": "Temperatura"
+          },
+          "max_tokens": {
+            "name": "Máx. Tokens"
+          },
+          "system_prompt": {
+            "name": "Indicaciones del Sistema"
+          },
+          "response_time": {
+            "name": "Último Tiempo de Respuesta"
+          },
+          "total_responses": {
+            "name": "Total de Respuestas"
+          },
+          "error_count": {
+            "name": "Conteo de Errores"
+          },
+          "last_error": {
+            "name": "Último Error"
+          },
+          "api_status": {
+            "name": "Estado de API"
+          },
+          "tokens_used": {
+            "name": "Total de Tokens Usados"
+          },
+          "average_response_time": {
+            "name": "Tiempo de Respuesta Promedio"
+          },
+          "last_request_time": {
+            "name": "Último Tiempo de Solicitud"
+          },
+          "is_processing": {
+            "name": "Estado de Procesamiento"
+          },
+          "is_rate_limited": {
+            "name": "Estado Limitado por Tasa"
+          },
+          "is_maintenance": {
+            "name": "Estado de Mantenimiento"
+          },
+          "api_version": {
+            "name": "Versión de API"
+          },
+          "endpoint_status": {
+            "name": "Estado del Endpoint"
+          },
+          "performance_metrics": {
+            "name": "Métricas de Rendimiento"
+          },
+          "history_size": {
+            "name": "Tamaño del Historial"
+          },
+          "uptime": {
+            "name": "Tiempo de Actividad"
+          },
+          "total_tokens": {
+            "name": "Total de Tokens"
+          },
+          "prompt_tokens": {
+            "name": "Tokens de Solicitud"
+          },
+          "completion_tokens": {
+            "name": "Tokens de Finalización"
+          },
+          "successful_requests": {
+            "name": "Solicitudes Exitosas"
+          },
+          "failed_requests": {
+            "name": "Solicitudes Fallidas"
+          },
+          "average_latency": {
+            "name": "Latencia Promedio"
+          },
+          "max_latency": {
+            "name": "Latencia Máxima"
+          },
+          "min_latency": {
+            "name": "Latencia Mínima"
+          }
+        }
+      }
+    }
+  }
+}

custom_components/ha_text_ai/translations/hi.json

@@ -0,0 +1,312 @@
+{
+  "config": {
+    "step": {
+      "provider": {
+        "title": "प्रदाता सेटिंग्स",
+        "description": "आपके द्वारा चुने गए एआई प्रदाता के लिए कनेक्शन विवरण प्रदान करें।",
+        "data": {
+          "name": "उदाहरण का नाम (जैसे, 'जीपीटी सहायक', 'क्लॉड सहायक')",
+          "api_key": "प्रमाणीकरण के लिए एपीआई कुंजी",
+          "model": "उपयोग करने के लिए एआई मॉडल",
+          "api_endpoint": "कस्टम एपीआई एंडपॉइंट यूआरएल (वैकल्पिक)",
+          "temperature": "प्रतिक्रिया की रचनात्मकता (0-2, कम = अधिक केंद्रित)",
+          "max_tokens": "प्रतिक्रिया की अधिकतम लंबाई (1-100000 टोकन)",
+          "request_interval": "अनुरोधों के बीच न्यूनतम समय (0.1-60 सेकंड)",
+          "api_timeout": "एपीआई अनुरोध टाइमआउट सेकंड में (5-600)",
+          "context_messages": "रखने के लिए संदर्भ संदेशों की संख्या (1-20)",
+          "max_history_size": "अधिकतम बातचीत इतिहास आकार (1-100)",
+          "allow_local_network": "स्थानीय नेटवर्क एंडपॉइंट की अनुमति दें (सेल्फ-होस्टेड प्रॉक्सी के लिए)"
+        }
+      },
+      "user": {
+        "title": "एचए टेक्स्ट एआई उदाहरण कॉन्फ़िगर करें",
+        "description": "अपने चुने हुए प्रदाता के साथ एक नया एआई सहायक उदाहरण सेट करें।",
+        "data": {
+          "name": "उदाहरण का नाम (जैसे, 'जीपीटी सहायक', 'क्लॉड सहायक')",
+          "api_key": "प्रमाणीकरण के लिए एपीआई कुंजी",
+          "model": "उपयोग करने के लिए एआई मॉडल",
+          "temperature": "प्रतिक्रिया की रचनात्मकता (0-2, कम = अधिक केंद्रित)",
+          "max_tokens": "प्रतिक्रिया की अधिकतम लंबाई (1-100000 टोकन)",
+          "api_endpoint": "कस्टम एपीआई एंडपॉइंट यूआरएल (वैकल्पिक)",
+          "api_provider": "एपीआई प्रदाता",
+          "request_interval": "अनुरोधों के बीच न्यूनतम समय (0.1-60 सेकंड)",
+          "api_timeout": "एपीआई अनुरोध टाइमआउट सेकंड में (5-600)",
+          "context_messages": "रखने के लिए संदर्भ संदेशों की संख्या (1-20)",
+          "max_history_size": "अधिकतम बातचीत इतिहास आकार (1-100)",
+          "allow_local_network": "स्थानीय नेटवर्क एंडपॉइंट की अनुमति दें (सेल्फ-होस्टेड प्रॉक्सी के लिए)"
+        }
+      }
+    },
+    "error": {
+      "history_storage_error": "इतिहास भंडारण प्रारंभ करने में विफल। अनुमतियों की जांच करें।",
+      "history_rotation_error": "इतिहास फ़ाइल घुमाने के दौरान त्रुटि।",
+      "history_file_access_error": "इतिहास भंडारण निर्देशिका तक पहुंच नहीं है।",
+      "name_exists": "इस नाम के साथ एक उदाहरण पहले से मौजूद है",
+      "invalid_name": "अमान्य उदाहरण नाम",
+      "invalid_auth": "प्रमाणीकरण विफल - अपनी एपीआई कुंजी की जांच करें",
+      "invalid_api_key": "अमान्य एपीआई कुंजी - कृपया अपनी क्रेडेंशियल्स की पुष्टि करें",
+      "cannot_connect": "एपीआई सेवा से कनेक्ट करने में विफल",
+      "invalid_model": "चुना हुआ मॉडल उपलब्ध नहीं है",
+      "rate_limit": "रेट सीमा पार",
+      "context_length": "संदर्भ लंबाई पार",
+      "rate_limit_exceeded": "एपीआई रेट सीमा पार",
+      "maintenance": "सेवा रखरखाव में है",
+      "invalid_response": "अमान्य एपीआई प्रतिक्रिया प्राप्त हुई",
+      "api_error": "एपीआई सेवा में त्रुटि हुई",
+      "timeout": "अनुरोध समय सीमा समाप्त",
+      "invalid_instance": "अमान्य उदाहरण निर्दिष्ट किया गया",
+      "unknown": "अप्रत्याशित त्रुटि हुई",
+      "empty": "नाम खाली नहीं हो सकता",
+      "invalid_characters": "नाम में केवल अक्षर, अंक, रिक्त स्थान, अंडरस्कोर और हाइफन हो सकते हैं",
+      "name_too_long": "नाम 50 अक्षरों या उससे कम होना चाहिए"
+    },
+    "abort": {
+      "already_configured": "उदाहरण पहले से कॉन्फ़िगर किया गया है"
+    }
+  },
+  "options": {
+    "step": {
+      "init": {
+        "title": "प्रदाता चुनें",
+        "description": "इस उदाहरण के लिए एआई प्रदाता चुनें। परिवर्तन सहेजने के बाद एकीकरण पुनः लोड होगा।",
+        "data": {
+          "api_provider": "एपीआई प्रदाता"
+        }
+      },
+      "settings": {
+        "title": "कनेक्शन और मॉडल सेटिंग्स",
+        "description": "एपीआई क्रेडेंशियल और मॉडल पैरामीटर कॉन्फ़िगर करें। एकीकरण पुनः लोड होने के बाद परिवर्तन प्रभावी होंगे।",
+        "data": {
+          "api_key": "एपीआई कुंजी",
+          "api_endpoint": "एपीआई एंडपॉइंट यूआरएल",
+          "model": "एआई मॉडल",
+          "temperature": "प्रतिक्रिया की रचनात्मकता (0-2)",
+          "max_tokens": "प्रतिक्रिया की अधिकतम लंबाई (1-100000)",
+          "request_interval": "न्यूनतम अनुरोध अंतराल (0.1-60 सेकंड)",
+          "api_timeout": "एपीआई अनुरोध टाइमआउट सेकंड में (5-600)",
+          "context_messages": "संदर्भ में शामिल करने के लिए पिछले संदेशों की संख्या (1-20)",
+          "max_history_size": "अधिकतम बातचीत इतिहास आकार (1-100)",
+          "allow_local_network": "स्थानीय नेटवर्क एंडपॉइंट की अनुमति दें (सेल्फ-होस्टेड प्रॉक्सी के लिए)"
+        }
+      }
+    }
+  },
+  "selector": {
+    "api_provider": {
+      "options": {
+        "openai": "OpenAI (अनुकूलित)",
+        "anthropic": "Anthropic (अनुकूलित)",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
+      }
+    }
+  },
+  "services": {
+    "ask_question": {
+      "name": "प्रश्न पूछें (HA Text AI)",
+      "description": "AI मॉडल को प्रश्न भेजें और विस्तृत उत्तर प्राप्त करें। यह सेवा अब प्रत्यक्ष रूप से प्रतिक्रिया डेटा वापस करती है, अलग टेक्स्ट सेंसर की आवश्यकता और 255 वर्ण की सीमा को समाप्त करती है। प्रतिक्रिया को बातचीत के इतिहास में भी संग्रहीत किया जाएगा।",
+      "fields": {
+        "instance": {
+          "name": "उदाहरण",
+          "description": "उपयोग करने के लिए एचए टेक्स्ट एआई उदाहरण का नाम"
+        },
+        "question": {
+          "name": "प्रश्न",
+          "description": "आपका प्रश्न या एआई सहायक के लिए प्रॉम्प्ट"
+        },
+        "context_messages": {
+          "name": "संदर्भ संदेश",
+          "description": "संदर्भ में शामिल करने के लिए पिछले संदेशों की संख्या (1-20)"
+        },
+        "system_prompt": {
+          "name": "सिस्टम प्रॉम्प्ट",
+          "description": "इस विशेष प्रश्न के लिए संदर्भ सेट करने के लिए वैकल्पिक सिस्टम प्रॉम्प्ट"
+        },
+        "model": {
+          "name": "मॉडल",
+          "description": "उपयोग करने के लिए एआई मॉडल का चयन करें (वैकल्पिक, डिफ़ॉल्ट सेटिंग को ओवरराइड करता है)"
+        },
+        "temperature": {
+          "name": "तापमान",
+          "description": "प्रतिक्रिया की रचनात्मकता को नियंत्रित करता है (0.0-2.0)"
+        },
+        "max_tokens": {
+          "name": "अधिकतम टोकन",
+          "description": "प्रतिक्रिया की अधिकतम लंबाई (1-100000 टोकन)"
+        },
+        "structured_output": {
+          "name": "संरचित आउटपुट",
+          "description": "JSON संरचित आउटपुट मोड सक्षम करें। सक्षम होने पर, AI प्रदान किए गए स्कीमा से मेल खाने वाले वैध JSON के साथ प्रतिक्रिया देगा।"
+        },
+        "json_schema": {
+          "name": "JSON स्कीमा",
+          "description": "अपेक्षित प्रतिक्रिया की संरचना को परिभाषित करने वाला JSON स्कीमा। structured_output सक्षम होने पर आवश्यक।"
+        }
+      }
+    },
+    "clear_history": {
+      "name": "इतिहास साफ करें",
+      "description": "बातचीत के इतिहास से सभी संग्रहीत प्रश्नों और प्रतिक्रियाओं को हटाएं",
+      "fields": {
+        "instance": {
+          "name": "उदाहरण",
+          "description": "इतिहास साफ़ करने के लिए एचए टेक्स्ट एआई उदाहरण का नाम"
+        }
+      }
+    },
+    "get_history": {
+      "name": "इतिहास प्राप्त करें",
+      "description": "वैकल्पिक फ़िल्टरिंग और छंटाई के साथ बातचीत का इतिहास प्राप्त करें",
+      "fields": {
+        "instance": {
+          "name": "उदाहरण",
+          "description": "इतिहास प्राप्त करने के लिए एचए टेक्स्ट एआई उदाहरण का नाम"
+        },
+        "limit": {
+          "name": "सीमा",
+          "description": "वापस करने के लिए बातचीत की संख्या (1-100)"
+        },
+        "filter_model": {
+          "name": "फिल्टर मॉडल",
+          "description": "विशिष्ट एआई मॉडल द्वारा बातचीत को फ़िल्टर करें"
+        },
+        "start_date": {
+          "name": "शुरुआत की तारीख",
+          "description": "इस दिन/समय से शुरू होने वाली बातचीत को फ़िल्टर करें"
+        },
+        "include_metadata": {
+          "name": "मेटाडेटा शामिल करें",
+          "description": "उपयोग किए गए टोकन, प्रतिक्रिया समय आदि जैसी अतिरिक्त जानकारी शामिल करें।"
+        },
+        "sort_order": {
+          "name": "छंटाई क्रम",
+          "description": "परिणामों के लिए छंटाई क्रम (नवीनतम या सबसे पुराना पहले)"
+        }
+      }
+    },
+    "set_system_prompt": {
+      "name": "सिस्टम प्रॉम्प्ट सेट करें",
+      "description": "सभी भविष्य की बातचीत के लिए डिफ़ॉल्ट सिस्टम व्यवहार निर्देश सेट करें",
+      "fields": {
+        "instance": {
+          "name": "उदाहरण",
+          "description": "सिस्टम प्रॉम्प्ट सेट करने के लिए एचए टेक्स्ट एआई उदाहरण का नाम"
+        },
+        "prompt": {
+          "name": "सिस्टम प्रॉम्प्ट",
+          "description": "निर्देश जो यह परिभाषित करते हैं कि एआई को कैसे व्यवहार करना चाहिए और प्रतिक्रिया देनी चाहिए"
+        }
+      }
+    }
+  },
+  "entity": {
+    "sensor": {
+      "ha_text_ai": {
+        "name": "{name}",
+        "state": {
+          "ready": "तैयार",
+          "processing": "प्रसंस्करण",
+          "error": "त्रुटि",
+          "disconnected": "असंयुक्त",
+          "rate_limited": "रेट सीमित",
+          "maintenance": "रखरखाव",
+          "initializing": "प्रारंभिककरण",
+          "retrying": "पुनः प्रयास कर रहा है",
+          "queued": "क्यू में"
+        },
+        "state_attributes": {
+          "question": {
+            "name": "अंतिम प्रश्न"
+          },
+          "response": {
+            "name": "अंतिम प्रतिक्रिया"
+          },
+          "model": {
+            "name": "वर्तमान मॉडल"
+          },
+          "temperature": {
+            "name": "तापमान"
+          },
+          "max_tokens": {
+            "name": "अधिकतम टोकन"
+          },
+          "system_prompt": {
+            "name": "सिस्टम प्रॉम्प्ट"
+          },
+          "response_time": {
+            "name": "अंतिम प्रतिक्रिया का समय"
+          },
+          "total_responses": {
+            "name": "कुल प्रतिक्रियाएं"
+          },
+          "error_count": {
+            "name": "त्रुटियों की संख्या"
+          },
+          "last_error": {
+            "name": "अंतिम त्रुटि"
+          },
+          "api_status": {
+            "name": "एपीआई स्थिति"
+          },
+          "tokens_used": {
+            "name": "कुल उपयोग किए गए टोकन"
+          },
+          "average_response_time": {
+            "name": "औसत प्रतिक्रिया समय"
+          },
+          "last_request_time": {
+            "name": "अंतिम अनुरोध का समय"
+          },
+          "is_processing": {
+            "name": "प्रसंस्करण स्थिति"
+          },
+          "is_rate_limited": {
+            "name": "रेट सीमित स्थिति"
+          },
+          "is_maintenance": {
+            "name": "रखरखाव स्थिति"
+          },
+          "api_version": {
+            "name": "एपीआई संस्करण"
+          },
+          "endpoint_status": {
+            "name": "एंडपॉइंट स्थिति"
+          },
+          "performance_metrics": {
+            "name": "प्रदर्शन मैट्रिक्स"
+          },
+          "history_size": {
+            "name": "इतिहास का आकार"
+          },
+          "uptime": {
+            "name": "अपटाइम"
+          },
+          "total_tokens": {
+            "name": "कुल टोकन"
+          },
+          "prompt_tokens": {
+            "name": "प्रॉम्प्ट टोकन"
+          },
+          "completion_tokens": {
+            "name": "पूर्णता टोकन"
+          },
+          "successful_requests": {
+            "name": "सफल अनुरोध"
+          },
+          "failed_requests": {
+            "name": "विफल अनुरोध"
+          },
+          "average_latency": {
+            "name": "औसत विलंबता"
+          },
+          "max_latency": {
+            "name": "अधिकतम विलंबता"
+          },
+          "min_latency": {
+            "name": "न्यूनतम विलंबता"
+          }
+        }
+      }
+    }
+  }
+}

custom_components/ha_text_ai/translations/it.json

@@ -0,0 +1,312 @@
+{
+  "config": {
+    "step": {
+      "provider": {
+        "title": "Impostazioni fornitore",
+        "description": "Fornisci i dettagli di connessione per il tuo fornitore di AI scelto.",
+        "data": {
+          "name": "Nome dell'istanza (es. 'Assistente GPT', 'Aiuto Claude')",
+          "api_key": "Chiave API per l'autenticazione",
+          "model": "Modello AI da utilizzare",
+          "api_endpoint": "URL dell'endpoint API personalizzato (opzionale)",
+          "temperature": "Creatività della risposta (0-2, più basso = più focalizzato)",
+          "max_tokens": "Lunghezza massima della risposta (1-100000 token)",
+          "request_interval": "Tempo minimo tra le richieste (0.1-60 secondi)",
+          "api_timeout": "Timeout della richiesta API in secondi (5-600)",
+          "context_messages": "Numero di messaggi di contesto da mantenere (1-20)",
+          "max_history_size": "Dimensione massima della cronologia delle conversazioni (1-100)",
+          "allow_local_network": "Consenti endpoint di rete locale (per proxy self-hosted)"
+        }
+      },
+      "user": {
+        "title": "Configura istanza AI di testo HA",
+        "description": "Imposta una nuova istanza di assistente AI con il fornitore selezionato.",
+        "data": {
+          "name": "Nome dell'istanza (es. 'Assistente GPT', 'Aiuto Claude')",
+          "api_key": "Chiave API per l'autenticazione",
+          "model": "Modello AI da utilizzare",
+          "temperature": "Creatività della risposta (0-2, più basso = più focalizzato)",
+          "max_tokens": "Lunghezza massima della risposta (1-100000 token)",
+          "api_endpoint": "URL dell'endpoint API personalizzato (opzionale)",
+          "api_provider": "Fornitore API",
+          "request_interval": "Tempo minimo tra le richieste (0.1-60 secondi)",
+          "api_timeout": "Timeout della richiesta API in secondi (5-600)",
+          "context_messages": "Numero di messaggi di contesto da mantenere (1-20)",
+          "max_history_size": "Dimensione massima della cronologia delle conversazioni (1-100)",
+          "allow_local_network": "Consenti endpoint di rete locale (per proxy self-hosted)"
+        }
+      }
+    },
+    "error": {
+      "history_storage_error": "Impossibile inizializzare la memorizzazione della cronologia. Controlla i permessi.",
+      "history_rotation_error": "Errore durante la rotazione del file di cronologia.",
+      "history_file_access_error": "Impossibile accedere alla directory di memorizzazione della cronologia.",
+      "name_exists": "Esiste già un'istanza con questo nome",
+      "invalid_name": "Nome dell'istanza non valido",
+      "invalid_auth": "Autenticazione fallita - controlla la tua chiave API",
+      "invalid_api_key": "Chiave API non valida - verifica le tue credenziali",
+      "cannot_connect": "Impossibile connettersi al servizio API",
+      "invalid_model": "Il modello selezionato non è disponibile",
+      "rate_limit": "Limite di frequenza superato",
+      "context_length": "Lunghezza del contesto superata",
+      "rate_limit_exceeded": "Limite di frequenza API superato",
+      "maintenance": "Il servizio è in manutenzione",
+      "invalid_response": "Risposta API non valida ricevuta",
+      "api_error": "Si è verificato un errore nel servizio API",
+      "timeout": "Richiesta scaduta",
+      "invalid_instance": "Istanze specificata non valida",
+      "unknown": "Si è verificato un errore imprevisto",
+      "empty": "Il nome non può essere vuoto",
+      "invalid_characters": "Il nome può contenere solo lettere, numeri, spazi, trattini bassi e trattini",
+      "name_too_long": "Il nome deve essere lungo 50 caratteri o meno"
+    },
+    "abort": {
+      "already_configured": "Istanze già configurata"
+    }
+  },
+  "options": {
+    "step": {
+      "init": {
+        "title": "Seleziona fornitore",
+        "description": "Scegli il fornitore AI per questa istanza. L'integrazione verrà ricaricata dopo aver salvato le modifiche.",
+        "data": {
+          "api_provider": "Fornitore API"
+        }
+      },
+      "settings": {
+        "title": "Impostazioni di connessione e modello",
+        "description": "Configura le credenziali API e i parametri del modello. Le modifiche avranno effetto dopo il ricaricamento dell'integrazione.",
+        "data": {
+          "api_key": "Chiave API",
+          "api_endpoint": "URL dell'endpoint API",
+          "model": "Modello AI",
+          "temperature": "Creatività della risposta (0-2)",
+          "max_tokens": "Lunghezza massima della risposta (1-100000)",
+          "request_interval": "Intervallo minimo di richiesta (0.1-60 secondi)",
+          "api_timeout": "Timeout della richiesta API in secondi (5-600)",
+          "context_messages": "Numero di messaggi precedenti da includere nel contesto (1-20)",
+          "max_history_size": "Dimensione massima della cronologia delle conversazioni (1-100)",
+          "allow_local_network": "Consenti endpoint di rete locale (per proxy self-hosted)"
+        }
+      }
+    }
+  },
+  "selector": {
+    "api_provider": {
+      "options": {
+        "openai": "OpenAI (compatibile)",
+        "anthropic": "Anthropic (compatibile)",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
+      }
+    }
+  },
+  "services": {
+    "ask_question": {
+      "name": "Fai una domanda (HA Text AI)",
+      "description": "Invia una domanda al modello AI e ricevi una risposta dettagliata. Questo servizio ora restituisce i dati di risposta direttamente, eliminando la necessità di sensori di testo separati e la limitazione di 255 caratteri. La risposta sarà anche memorizzata nella cronologia delle conversazioni.",
+      "fields": {
+        "instance": {
+          "name": "Istanze",
+          "description": "Nome dell'istanza HA Text AI da utilizzare"
+        },
+        "question": {
+          "name": "Domanda",
+          "description": "La tua domanda o richiesta per l'assistente AI"
+        },
+        "context_messages": {
+          "name": "Messaggi di contesto",
+          "description": "Numero di messaggi precedenti da includere nel contesto (1-20)"
+        },
+        "system_prompt": {
+          "name": "Prompt di sistema",
+          "description": "Prompt di sistema opzionale per impostare il contesto per questa specifica domanda"
+        },
+        "model": {
+          "name": "Modello",
+          "description": "Seleziona il modello AI da utilizzare (opzionale, sovrascrive l'impostazione predefinita)"
+        },
+        "temperature": {
+          "name": "Temperatura",
+          "description": "Controlla la creatività della risposta (0.0-2.0)"
+        },
+        "max_tokens": {
+          "name": "Token massimi",
+          "description": "Lunghezza massima della risposta (1-100000 token)"
+        },
+        "structured_output": {
+          "name": "Output Strutturato",
+          "description": "Abilita la modalità di output JSON strutturato. Quando abilitato, l'IA risponderà con JSON valido corrispondente allo schema fornito."
+        },
+        "json_schema": {
+          "name": "Schema JSON",
+          "description": "Schema JSON che definisce la struttura della risposta attesa. Richiesto quando structured_output è abilitato."
+        }
+      }
+    },
+    "clear_history": {
+      "name": "Cancella cronologia",
+      "description": "Elimina tutte le domande e risposte memorizzate dalla cronologia delle conversazioni",
+      "fields": {
+        "instance": {
+          "name": "Istanze",
+          "description": "Nome dell'istanza HA Text AI per cui cancellare la cronologia"
+        }
+      }
+    },
+    "get_history": {
+      "name": "Ottieni cronologia",
+      "description": "Recupera la cronologia delle conversazioni con opzioni di filtro e ordinamento",
+      "fields": {
+        "instance": {
+          "name": "Istanze",
+          "description": "Nome dell'istanza HA Text AI da cui recuperare la cronologia"
+        },
+        "limit": {
+          "name": "Limite",
+          "description": "Numero di conversazioni da restituire (1-100)"
+        },
+        "filter_model": {
+          "name": "Filtra modello",
+          "description": "Filtra le conversazioni per modello AI specifico"
+        },
+        "start_date": {
+          "name": "Data di inizio",
+          "description": "Filtra le conversazioni a partire da questa data/ora"
+        },
+        "include_metadata": {
+          "name": "Includi metadati",
+          "description": "Includi informazioni aggiuntive come token utilizzati, tempo di risposta, ecc."
+        },
+        "sort_order": {
+          "name": "Ordine di ordinamento",
+          "description": "Ordine di ordinamento per i risultati (più recenti o più vecchi per primi)"
+        }
+      }
+    },
+    "set_system_prompt": {
+      "name": "Imposta prompt di sistema",
+      "description": "Imposta le istruzioni di comportamento predefinite per tutte le future conversazioni",
+      "fields": {
+        "instance": {
+          "name": "Istanze",
+          "description": "Nome dell'istanza HA Text AI per cui impostare il prompt di sistema"
+        },
+        "prompt": {
+          "name": "Prompt di sistema",
+          "description": "Istruzioni che definiscono come l'AI dovrebbe comportarsi e rispondere"
+        }
+      }
+    }
+  },
+  "entity": {
+    "sensor": {
+      "ha_text_ai": {
+        "name": "{name}",
+        "state": {
+          "ready": "Pronto",
+          "processing": "Elaborazione",
+          "error": "Errore",
+          "disconnected": "Disconnesso",
+          "rate_limited": "Limite di frequenza",
+          "maintenance": "Manutenzione",
+          "initializing": "Inizializzazione",
+          "retrying": "Riprova",
+          "queued": "In coda"
+        },
+        "state_attributes": {
+          "question": {
+            "name": "Ultima domanda"
+          },
+          "response": {
+            "name": "Ultima risposta"
+          },
+          "model": {
+            "name": "Modello attuale"
+          },
+          "temperature": {
+            "name": "Temperatura"
+          },
+          "max_tokens": {
+            "name": "Token massimi"
+          },
+          "system_prompt": {
+            "name": "Prompt di sistema"
+          },
+          "response_time": {
+            "name": "Ultimo tempo di risposta"
+          },
+          "total_responses": {
+            "name": "Risposte totali"
+          },
+          "error_count": {
+            "name": "Conteggio errori"
+          },
+          "last_error": {
+            "name": "Ultimo errore"
+          },
+          "api_status": {
+            "name": "Stato API"
+          },
+          "tokens_used": {
+            "name": "Token totali utilizzati"
+          },
+          "average_response_time": {
+            "name": "Tempo medio di risposta"
+          },
+          "last_request_time": {
+            "name": "Ultimo tempo di richiesta"
+          },
+          "is_processing": {
+            "name": "Stato di elaborazione"
+          },
+          "is_rate_limited": {
+            "name": "Stato limite di frequenza"
+          },
+          "is_maintenance": {
+            "name": "Stato di manutenzione"
+          },
+          "api_version": {
+            "name": "Versione API"
+          },
+          "endpoint_status": {
+            "name": "Stato dell'endpoint"
+          },
+          "performance_metrics": {
+            "name": "Metriche di prestazione"
+          },
+          "history_size": {
+            "name": "Dimensione della cronologia"
+          },
+          "uptime": {
+            "name": "Tempo di attività"
+          },
+          "total_tokens": {
+            "name": "Token totali"
+          },
+          "prompt_tokens": {
+            "name": "Token di prompt"
+          },
+          "completion_tokens": {
+            "name": "Token di completamento"
+          },
+          "successful_requests": {
+            "name": "Richieste riuscite"
+          },
+          "failed_requests": {
+            "name": "Richieste fallite"
+          },
+          "average_latency": {
+            "name": "Latenza media"
+          },
+          "max_latency": {
+            "name": "Latenza massima"
+          },
+          "min_latency": {
+            "name": "Latenza minima"
+          }
+        }
+      }
+    }
+  }
+}

custom_components/ha_text_ai/translations/ru.json

@@ -2,99 +2,145 @@
   "config": {
     "step": {
       "provider": {
-        "title": "Выбор поставщика ИИ",
-        "description": "Выберите поставщика услуг ИИ для этой инстанции.",
+        "title": "Настройки провайдера",
+        "description": "Укажите параметры подключения для выбранного провайдера ИИ.",
         "data": {
-          "api_provider": "Поставщик API",
-          "context_messages": "Количество контекстных сообщений для сохранения (1-20)"
+          "name": "Название экземпляра (например, 'GPT Помощник', 'Клод Ассистент')",
+          "api_key": "API-ключ для аутентификации",
+          "model": "Модель ИИ для использования",
+          "api_endpoint": "Пользовательский URL-адрес конечной точки API (необязательно)",
+          "temperature": "Креативность ответа (0-2, меньше = более сфокусированно)",
+          "max_tokens": "Максимальная длина ответа (1-100000 токенов)",
+          "request_interval": "Минимальный интервал между запросами (0.1-60 секунд)",
+          "api_timeout": "Таймаут API-запроса в секундах (5-600)",
+          "context_messages": "Количество сохраняемых контекстных сообщений (1-20)",
+          "max_history_size": "Максимальный размер истории разговора (1-100)",
+          "allow_local_network": "Разрешить локальные сетевые адреса (для self-hosted прокси)"
         }
       },
       "user": {
-        "title": "Настройка инстанции HA Text AI",
-        "description": "Настройте новую инстанцию помощника ИИ с выбранным вами поставщиком.",
+        "title": "Настройка экземпляра текстового ИИ для Home Assistant",
+        "description": "Настройте новый экземпляр ИИ-помощника с выбранным провайдером.",
         "data": {
-          "name": "Имя инстанции (например, 'GPT Assistant', 'Claude Helper')",
+          "name": "Название экземпляра (например, 'GPT Помощник', 'Клод Ассистент')",
           "api_key": "API-ключ для аутентификации",
-          "model": "Используемая модель ИИ",
-          "temperature": "Креативность ответа (0-2, чем ниже, тем больше фокусировки)",
-          "max_tokens": "Максимальная длина ответа (1-4096 токенов)",
+          "model": "Модель ИИ для использования",
+          "temperature": "Креативность ответа (0-2, меньше = более сфокусированно)",
+          "max_tokens": "Максимальная длина ответа (1-100000 токенов)",
           "api_endpoint": "Пользовательский URL-адрес конечной точки API (необязательно)",
-          "api_provider": "Поставщик API",
-          "request_interval": "Минимальное время между запросами (0,1-60 секунд)",
-          "context_messages": "Количество контекстных сообщений для сохранения (1-20)",
-          "max_history_size": "Максимальный размер истории разговора (1-100)"
+          "api_provider": "Провайдер API",
+          "request_interval": "Минимальный интервал между запросами (0.1-60 секунд)",
+          "api_timeout": "Таймаут API-запроса в секундах (5-600)",
+          "context_messages": "Количество сохраняемых контекстных сообщений (1-20)",
+          "max_history_size": "Максимальный размер истории разговора (1-100)",
+          "allow_local_network": "Разрешить локальные сетевые адреса (для self-hosted прокси)"
         }
       }
     },
     "error": {
-      "name_exists": "Инстанция с таким именем уже существует",
-      "invalid_name": "Некорректное имя инстанции",
-      "invalid_auth": "Аутентификация не удалась - проверьте ваш API-ключ",
-      "invalid_api_key": "Неверный API-ключ - проверьте ваши учетные данные",
-      "cannot_connect": "Не удалось подключиться к службе API",
+      "history_storage_error": "Не удалось инициализировать хранилище истории. Проверьте разрешения.",
+      "history_rotation_error": "Ошибка при ротации файла истории.",
+      "history_file_access_error": "Невозможно получить доступ к директории хранения истории.",
+      "name_exists": "Экземпляр с таким именем уже существует",
+      "invalid_name": "Недопустимое имя экземпляра",
+      "invalid_auth": "Ошибка аутентификации - проверьте API-ключ",
+      "api_key_required": "Необходимо ввести API-ключ при смене провайдера или эндпоинта",
+      "invalid_api_key": "Недопустимый API-ключ - пожалуйста, проверьте учетные данные",
+      "cannot_connect": "Не удалось подключиться к сервису API",
       "invalid_model": "Выбранная модель недоступна",
       "rate_limit": "Превышен лимит запросов",
       "context_length": "Превышена длина контекста",
       "rate_limit_exceeded": "Превышен лимит запросов API",
       "maintenance": "Сервис находится на техническом обслуживании",
-      "invalid_response": "Получен неверный ответ API",
-      "api_error": "Произошла ошибка службы API",
-      "timeout": "Запрос превысил время ожидания",
-      "invalid_instance": "Указана неверная инстанция",
+      "invalid_response": "Получен некорректный ответ API",
+      "api_error": "Произошла ошибка сервиса API",
+      "timeout": "Время ожидания запроса истекло",
+      "invalid_instance": "Указан некорректный экземпляр",
       "unknown": "Произошла непредвиденная ошибка",
       "empty": "Имя не может быть пустым",
-      "invalid_characters": "Имя может содержать только буквы, цифры, пробелы, подчеркивания и дефисы",
-      "name_too_long": "Имя должно быть не более 50 символов"
+      "name_too_long": "Имя должно быть не длиннее 50 символов"
+    },
+    "abort": {
+      "already_configured": "Экземпляр уже настроен"
     }
   },
   "options": {
     "step": {
       "init": {
-        "title": "Обновление настроек инстанции",
-        "description": "Измените настройки для этой инстанции помощника ИИ.",
+        "title": "Выбор провайдера",
+        "description": "Выберите провайдера ИИ для этого экземпляра. Интеграция перезагрузится после сохранения изменений.",
         "data": {
+          "api_provider": "Провайдер API"
+        }
+      },
+      "settings": {
+        "title": "Настройки подключения и модели",
+        "description": "Настройте учётные данные API и параметры модели. Изменения вступят в силу после перезагрузки интеграции.",
+        "data": {
+          "api_key": "API-ключ",
+          "api_endpoint": "URL конечной точки API",
           "model": "Модель ИИ",
           "temperature": "Креативность ответа (0-2)",
-          "max_tokens": "Максимальная длина ответа (1-4096)",
-          "request_interval": "Минимальный интервал между запросами (0,1-60 секунд)",
+          "max_tokens": "Максимальная длина ответа (1-100000)",
+          "request_interval": "Минимальный интервал между запросами (0.1-60 секунд)",
+          "api_timeout": "Таймаут API-запроса в секундах (5-600)",
           "context_messages": "Количество предыдущих сообщений для включения в контекст (1-20)",
-          "max_history_size": "Максимальный размер истории разговора (1-100)"
+          "max_history_size": "Максимальный размер истории разговора (1-100)",
+          "allow_local_network": "Разрешить локальные сетевые адреса (для self-hosted прокси)"
         }
       }
     }
   },
+  "selector": {
+    "api_provider": {
+      "options": {
+        "openai": "OpenAI (совместимый)",
+        "anthropic": "Anthropic (совместимый)",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
+      }
+    }
+  },
   "services": {
     "ask_question": {
       "name": "Задать вопрос (HA Text AI)",
-      "description": "Отправьте вопрос модели ИИ и получите подробный ответ. Ответ будет сохранен в истории разговора и может быть извлечен позже.",
+      "description": "Отправить вопрос модели ИИ и получить подробный ответ. Сервис теперь возвращает данные ответа напрямую, устраняя необходимость в отдельных текстовых сенсорах и ограничение в 255 символов. Ответ также будет сохранен в истории разговора.",
       "fields": {
         "instance": {
-          "name": "Инстанция",
-          "description": "Имя используемой инстанции HA Text AI"
+          "name": "Экземпляр",
+          "description": "Название экземпляра текстового ИИ для использования"
         },
         "question": {
           "name": "Вопрос",
-          "description": "Ваш вопрос или запрос к помощнику ИИ"
+          "description": "Ваш вопрос или запрос к ИИ-помощнику"
         },
         "context_messages": {
           "name": "Контекстные сообщения",
           "description": "Количество предыдущих сообщений для включения в контекст (1-20)"
         },
         "system_prompt": {
-          "name": "Системный запрос",
-          "description": "Необязательный системный запрос для установки контекста для этого конкретного вопроса"
+          "name": "Системный промпт",
+          "description": "Необязательный системный промпт для установки контекста для этого конкретного вопроса"
         },
         "model": {
           "name": "Модель",
-          "description": "Выберите модель ИИ для использования (необязательно, переопределяет настройку по умолчанию)"
+          "description": "Выберите модель ИИ для использования (необязательно, переопределяет настройки по умолчанию)"
         },
         "temperature": {
           "name": "Температура",
-          "description": "Управляет креативностью ответа (0.0-2.0)"
+          "description": "Управление креативностью ответа (0.0-2.0)"
         },
         "max_tokens": {
-          "name": "Макс. токенов",
-          "description": "Максимальная длина ответа (1-4096 токенов)"
+          "name": "Максимум токенов",
+          "description": "Максимальная длина ответа (1-100000 токенов)"
+        },
+        "structured_output": {
+          "name": "Структурированный вывод",
+          "description": "Включить режим структурированного JSON-вывода. При включении ИИ будет отвечать валидным JSON, соответствующим указанной схеме."
+        },
+        "json_schema": {
+          "name": "JSON Schema",
+          "description": "JSON-схема, определяющая структуру ожидаемого ответа. Обязательна при включении structured_output."
         }
       }
     },
@@ -103,18 +149,18 @@
       "description": "Удалить все сохраненные вопросы и ответы из истории разговора",
       "fields": {
         "instance": {
-          "name": "Инстанция",
-          "description": "Имя инстанции HA Text AI, для которой нужно очистить историю"
+          "name": "Экземпляр",
+          "description": "Название экземпляра текстового ИИ для очистки истории"
         }
       }
     },
     "get_history": {
       "name": "Получить историю",
-      "description": "Извлечь историю разговора с возможностью фильтрации и сортировки",
+      "description": "Получить историю разговора с дополнительной фильтрацией и сортировкой",
       "fields": {
         "instance": {
-          "name": "Инстанция",
-          "description": "Имя инстанции HA Text AI, из которой нужно получить историю"
+          "name": "Экземпляр",
+          "description": "Название экземпляра текстового ИИ для получения истории"
         },
         "limit": {
           "name": "Лимит",
@@ -122,33 +168,33 @@
         },
         "filter_model": {
           "name": "Фильтр модели",
-          "description": "Фильтрация разговоров по определенной модели ИИ"
+          "description": "Фильтрация разговоров по конкретной модели ИИ"
         },
         "start_date": {
-          "name": "Дата начала",
-          "description": "Фильтрация разговоров, начиная с этой даты/времени"
+          "name": "Начальная дата",
+          "description": "Фильтрация разговоров, начиная с указанной даты/времени"
         },
         "include_metadata": {
           "name": "Включить метаданные",
-          "description": "Включить дополнительную информацию, такую как использованные токены, время ответа и т.д."
+          "description": "Включить дополнительную информацию, например, использованные токены, время ответа и т.д."
         },
         "sort_order": {
           "name": "Порядок сортировки",
-          "description": "Порядок сортировки результатов (самые новые или самые старые)"
+          "description": "Порядок сортировки результатов (сначала новые или старые)"
         }
       }
     },
     "set_system_prompt": {
-      "name": "Установить системный запрос",
-      "description": "Установить инструкции по умолчанию для поведения системы для всех будущих разговоров",
+      "name": "Установить системный промпт",
+      "description": "Установить инструкции по умолчанию для поведения ИИ во всех будущих разговорах",
       "fields": {
         "instance": {
-          "name": "Инстанция",
-          "description": "Имя инстанции HA Text AI, для которой нужно установить системный запрос"
+          "name": "Экземпляр",
+          "description": "Название экземпляра текстового ИИ для установки системного промпта"
         },
         "prompt": {
-          "name": "Системный запрос",
-          "description": "Инструкции, определяющие, как ИИ должен себя вести и отвечать"
+          "name": "Системный промпт",
+          "description": "Инструкции, определяющие, как ИИ должен вести себя и отвечать"
         }
       }
     }
@@ -165,8 +211,7 @@
           "rate_limited": "Лимит запросов",
           "maintenance": "Техническое обслуживание",
           "initializing": "Инициализация",
-          "retrying": "Повторная попытка",
-          "queued": "В очереди"
+          "retrying": "Повторная попытка"
         },
         "state_attributes": {
           "question": {
@@ -182,10 +227,10 @@
             "name": "Температура"
           },
           "max_tokens": {
-            "name": "Макс. токенов"
+            "name": "Максимум токенов"
           },
           "system_prompt": {
-            "name": "Системный запрос"
+            "name": "Системный промпт"
           },
           "response_time": {
             "name": "Время последнего ответа"
@@ -203,7 +248,7 @@
             "name": "Статус API"
           },
           "tokens_used": {
-            "name": "Использовано токенов всего"
+            "name": "Всего использовано токенов"
           },
           "average_response_time": {
             "name": "Среднее время ответа"
@@ -215,10 +260,10 @@
             "name": "Статус обработки"
           },
           "is_rate_limited": {
-            "name": "Статус ограничения запросов"
+            "name": "Статус лимита запросов"
           },
           "is_maintenance": {
-            "name": "Статус технического обслуживания"
+            "name": "Статус обслуживания"
           },
           "api_version": {
             "name": "Версия API"
@@ -227,7 +272,7 @@
             "name": "Статус конечной точки"
           },
           "performance_metrics": {
-            "name": "Метрики производительности"
+            "name": "Показатели производительности"
           },
           "history_size": {
             "name": "Размер истории"
@@ -239,16 +284,16 @@
             "name": "Всего токенов"
           },
           "prompt_tokens": {
-            "name": "Токены запроса"
+            "name": "Токены промпта"
           },
           "completion_tokens": {
             "name": "Токены завершения"
           },
           "successful_requests": {
-            "name": "Успешных запросов"
+            "name": "Успешные запросы"
           },
           "failed_requests": {
-            "name": "Неудачных запросов"
+            "name": "Неудачные запросы"
           },
           "average_latency": {
             "name": "Средняя задержка"
@@ -258,6 +303,24 @@
           },
           "min_latency": {
             "name": "Минимальная задержка"
+          },
+          "last_model": {
+            "name": "Последняя использованная модель"
+          },
+          "last_timestamp": {
+            "name": "Время последнего ответа"
+          },
+          "instance_name": {
+            "name": "Имя экземпляра"
+          },
+          "normalized_name": {
+            "name": "Нормализованное имя"
+          },
+          "last_error": {
+            "name": "Последняя ошибка"
+          },
+          "conversation_history": {
+            "name": "История разговоров"
           }
         }
       }

custom_components/ha_text_ai/translations/sr.json

@@ -0,0 +1,312 @@
+{
+  "config": {
+    "step": {
+      "provider": {
+        "title": "Подешавања провајдера",
+        "description": "Обезбедите детаље о вези за изабраног AI провајдера.",
+        "data": {
+          "name": "Име инстанце (нпр. 'GPT Асистент', 'Claude Помоћник')",
+          "api_key": "API кључ за аутентификацију",
+          "model": "AI модел који ће се користити",
+          "api_endpoint": "Прилагођени URL API крајње тачке (опционо)",
+          "temperature": "Креативност одговора (0-2, нижа = фокусираније)",
+          "max_tokens": "Максимална дужина одговора (1-100000 токена)",
+          "request_interval": "Минимално време између захтева (0.1-60 секунди)",
+          "api_timeout": "Временско ограничење API захтева у секундама (5-600)",
+          "context_messages": "Број контекстуалних порука које треба задржати (1-20)",
+          "max_history_size": "Максимална величина историје разговора (1-100)",
+          "allow_local_network": "Дозволи локалне мрежне адресе (за self-hosted проксије)"
+        }
+      },
+      "user": {
+        "title": "Конфигуришите HA Text AI инстанцу",
+        "description": "Подесите нову AI асистент инстанцу са изабраним провајдером.",
+        "data": {
+          "name": "Име инстанце (нпр. 'GPT Асистент', 'Claude Помоћник')",
+          "api_key": "API кључ за аутентификацију",
+          "model": "AI модел који ће се користити",
+          "temperature": "Креативност одговора (0-2, нижа = фокусираније)",
+          "max_tokens": "Максимална дужина одговора (1-100000 токена)",
+          "api_endpoint": "Прилагођени URL API крајње тачке (опционо)",
+          "api_provider": "API провајдер",
+          "request_interval": "Минимално време између захтева (0.1-60 секунди)",
+          "api_timeout": "Временско ограничење API захтева у секундама (5-600)",
+          "context_messages": "Број контекстуалних порука које треба задржати (1-20)",
+          "max_history_size": "Максимална величина историје разговора (1-100)",
+          "allow_local_network": "Дозволи локалне мрежне адресе (за self-hosted проксије)"
+        }
+      }
+    },
+    "error": {
+      "history_storage_error": "Неуспела инициализација складишта историје. Проверите дозволе.",
+      "history_rotation_error": "Грешка током ротације историјских датотека.",
+      "history_file_access_error": "Немогуће приступити директоријуму складишта историје.",
+      "name_exists": "Инстанца са овим именом већ постоји",
+      "invalid_name": "Неважеће име инстанце",
+      "invalid_auth": "Аутентификација није успела - проверите ваш API кључ",
+      "invalid_api_key": "Неважећи API кључ - молимо проверите ваше акредитиве",
+      "cannot_connect": "Неуспело повезивање са API сервисом",
+      "invalid_model": "Изабрани модел није доступан",
+      "rate_limit": "Пређена граница захтева",
+      "context_length": "Дужина контекста пређена",
+      "rate_limit_exceeded": "Пређена граница API захтева",
+      "maintenance": "Сервис је у одржавању",
+      "invalid_response": "Примљен неважећи API одговор",
+      "api_error": "Дошло је до грешке у API сервису",
+      "timeout": "Време захтева је истекло",
+      "invalid_instance": "Неважећа инстанца је назначена",
+      "unknown": "Дошло је до неочекиване грешке",
+      "empty": "Име не може бити празно",
+      "invalid_characters": "Име може садржавати само слова, цифре, размаке, подцрта и цртице",
+      "name_too_long": "Име мора бити 50 знакова или мање"
+    },
+    "abort": {
+      "already_configured": "Инстанца је већ конфигурисана"
+    }
+  },
+  "options": {
+    "step": {
+      "init": {
+        "title": "Изаберите провајдера",
+        "description": "Изаберите AI провајдера за ову инстанцу. Интеграција ће се поново учитати након чувања измена.",
+        "data": {
+          "api_provider": "API провајдер"
+        }
+      },
+      "settings": {
+        "title": "Подешавања везе и модела",
+        "description": "Конфигуришите API акредитиве и параметре модела. Промене ће ступити на снагу након поновног учитавања интеграције.",
+        "data": {
+          "api_key": "API кључ",
+          "api_endpoint": "URL API крајње тачке",
+          "model": "AI модел",
+          "temperature": "Креативност одговора (0-2)",
+          "max_tokens": "Максимална дужина одговора (1-100000)",
+          "request_interval": "Минимално време између захтева (0.1-60 секунди)",
+          "api_timeout": "Временско ограничење API захтева у секундама (5-600)",
+          "context_messages": "Број претходних порука које треба укључити у контекст (1-20)",
+          "max_history_size": "Максимална величина историје разговора (1-100)",
+          "allow_local_network": "Дозволи локалне мрежне адресе (за self-hosted проксије)"
+        }
+      }
+    }
+  },
+  "selector": {
+    "api_provider": {
+      "options": {
+        "openai": "OpenAI (компатибилан)",
+        "anthropic": "Anthropic (компатибилан)",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
+      }
+    }
+  },
+  "services": {
+    "ask_question": {
+      "name": "Поставите питање (HA Text AI)",
+      "description": "Пошаљите питање AI моделу и добијте детаљан одговор. Овај сервис сада враћа податке одговора директно, елиминишући потребу за засебним текстуалним сензорима и ограничење од 255 карактера. Одговор ће такође бити сачуван у историји разговора.",
+      "fields": {
+        "instance": {
+          "name": "Инстанца",
+          "description": "Име HA Text AI инстанце коју ћете користити"
+        },
+        "question": {
+          "name": "Питање",
+          "description": "Ваше питање или упит за AI асистента"
+        },
+        "context_messages": {
+          "name": "Контекстуалне поруке",
+          "description": "Број претходних порука које треба укључити у контекст (1-20)"
+        },
+        "system_prompt": {
+          "name": "Системски упит",
+          "description": "Опционални системски упит за постављање контекста за ово конкретно питање"
+        },
+        "model": {
+          "name": "Модел",
+          "description": "Изаберите AI модел који ћете користити (опционо, надмашује подразумевану поставку)"
+        },
+        "temperature": {
+          "name": "Температура",
+          "description": "Контролише креативност одговора (0.0-2.0)"
+        },
+        "max_tokens": {
+          "name": "Максимални токени",
+          "description": "Максимална дужина одговора (1-100000 токена)"
+        },
+        "structured_output": {
+          "name": "Структурисани излаз",
+          "description": "Омогући JSON структурисани излаз. Када је омогућено, AI ће одговарати валидним JSON-ом који одговара датој шеми."
+        },
+        "json_schema": {
+          "name": "JSON шема",
+          "description": "JSON шема која дефинише структуру очекиваног одговора. Обавезна када је structured_output омогућен."
+        }
+      }
+    },
+    "clear_history": {
+      "name": "Обриши историју",
+      "description": "Избришите све сачуване питања и одговоре из историје разговора",
+      "fields": {
+        "instance": {
+          "name": "Инстанца",
+          "description": "Име HA Text AI инстанце за коју желите да обришете историју"
+        }
+      }
+    },
+    "get_history": {
+      "name": "Добијте историју",
+      "description": "Повратите историју разговора уз опционално филтрирање и сортирање",
+      "fields": {
+        "instance": {
+          "name": "Инстанца",
+          "description": "Име HA Text AI инстанце из које желите да добијете историју"
+        },
+        "limit": {
+          "name": "Лимит",
+          "description": "Број разговора које треба вратити (1-100)"
+        },
+        "filter_model": {
+          "name": "Филтер модел",
+          "description": "Филтрирајте разговоре по одређеном AI моделу"
+        },
+        "start_date": {
+          "name": "Датум почетка",
+          "description": "Филтрирајте разговоре који почињу од овог датума/времена"
+        },
+        "include_metadata": {
+          "name": "Укључи метаподатке",
+          "description": "Укључите додатне информације као што су коришћени токени, време одговора итд."
+        },
+        "sort_order": {
+          "name": "Редослед сортирања",
+          "description": "Редослед сортирања за резултате (најновији или најстарији први)"
+        }
+      }
+    },
+    "set_system_prompt": {
+      "name": "Поставите системски упит",
+      "description": "Поставите подразумеване инструкције за системско понашање за све будуће разговоре",
+      "fields": {
+        "instance": {
+          "name": "Инстанца",
+          "description": "Име HA Text AI инстанце за коју желите да поставите системски упит"
+        },
+        "prompt": {
+          "name": "Системски упит",
+          "description": "Инструкције које дефинишу како AI треба да се понаша и одговара"
+        }
+      }
+    }
+  },
+  "entity": {
+    "sensor": {
+      "ha_text_ai": {
+        "name": "{name}",
+        "state": {
+          "ready": "Спремно",
+          "processing": "Обрада",
+          "error": "Грешка",
+          "disconnected": "Искључено",
+          "rate_limited": "Ограничење захтева",
+          "maintenance": "Одржавање",
+          "initializing": "Инициализује се",
+          "retrying": "Покушава поново",
+          "queued": "У реду"
+        },
+        "state_attributes": {
+          "question": {
+            "name": "Последње питање"
+          },
+          "response": {
+            "name": "Последњи одговор"
+          },
+          "model": {
+            "name": "Тренутни модел"
+          },
+          "temperature": {
+            "name": "Температура"
+          },
+          "max_tokens": {
+            "name": "Максимални токени"
+          },
+          "system_prompt": {
+            "name": "Системски упит"
+          },
+          "response_time": {
+            "name": "Време последњег одговора"
+          },
+          "total_responses": {
+            "name": "Укупно одговора"
+          },
+          "error_count": {
+            "name": "Број грешака"
+          },
+          "last_error": {
+            "name": "Последња грешка"
+          },
+          "api_status": {
+            "name": "Статус API"
+          },
+          "tokens_used": {
+            "name": "Укупно коришћени токени"
+          },
+          "average_response_time": {
+            "name": "Просечно време одговора"
+          },
+          "last_request_time": {
+            "name": "Време последњег захтева"
+          },
+          "is_processing": {
+            "name": "Статус обраде"
+          },
+          "is_rate_limited": {
+            "name": "Статус ограничења захтева"
+          },
+          "is_maintenance": {
+            "name": "Статус одржавања"
+          },
+          "api_version": {
+            "name": "Верзија API"
+          },
+          "endpoint_status": {
+            "name": "Статус крајње тачке"
+          },
+          "performance_metrics": {
+            "name": "Перформансне метрике"
+          },
+          "history_size": {
+            "name": "Величина историје"
+          },
+          "uptime": {
+            "name": "Уптиме"
+          },
+          "total_tokens": {
+            "name": "Укупно токена"
+          },
+          "prompt_tokens": {
+            "name": "Токени упита"
+          },
+          "completion_tokens": {
+            "name": "Токени завршетка"
+          },
+          "successful_requests": {
+            "name": "Успешни захтеви"
+          },
+          "failed_requests": {
+            "name": "Неуспешни захтеви"
+          },
+          "average_latency": {
+            "name": "Просечна латенција"
+          },
+          "max_latency": {
+            "name": "Максимална латенција"
+          },
+          "min_latency": {
+            "name": "Минимална латенција"
+          }
+        }
+      }
+    }
+  }
+}

custom_components/ha_text_ai/translations/zh.json

@@ -0,0 +1,312 @@
+{
+  "config": {
+    "step": {
+      "provider": {
+        "title": "提供者设置",
+        "description": "提供所选AI提供者的连接详细信息。",
+        "data": {
+          "name": "实例名称（例如，'GPT助手'，'Claude助手'）",
+          "api_key": "用于身份验证的API密钥",
+          "model": "要使用的AI模型",
+          "api_endpoint": "自定义API端点URL（可选）",
+          "temperature": "响应创造力（0-2，越低越专注）",
+          "max_tokens": "最大响应长度（1-100000个标记）",
+          "request_interval": "请求之间的最小时间（0.1-60秒）",
+          "api_timeout": "API请求超时时间（5-600秒）",
+          "context_messages": "保留的上下文消息数量（1-20）",
+          "max_history_size": "最大对话历史大小（1-100）",
+          "allow_local_network": "允许本地网络端点（用于自托管代理）"
+        }
+      },
+      "user": {
+        "title": "配置HA文本AI实例",
+        "description": "使用所选提供者设置新的AI助手实例。",
+        "data": {
+          "name": "实例名称（例如，'GPT助手'，'Claude助手'）",
+          "api_key": "用于身份验证的API密钥",
+          "model": "要使用的AI模型",
+          "temperature": "响应创造力（0-2，越低越专注）",
+          "max_tokens": "最大响应长度（1-100000个标记）",
+          "api_endpoint": "自定义API端点URL（可选）",
+          "api_provider": "API提供者",
+          "request_interval": "请求之间的最小时间（0.1-60秒）",
+          "api_timeout": "API请求超时时间（5-600秒）",
+          "context_messages": "保留的上下文消息数量（1-20）",
+          "max_history_size": "最大对话历史大小（1-100）",
+          "allow_local_network": "允许本地网络端点（用于自托管代理）"
+        }
+      }
+    },
+    "error": {
+      "history_storage_error": "无法初始化历史存储。检查权限。",
+      "history_rotation_error": "历史文件轮换时出错。",
+      "history_file_access_error": "无法访问历史存储目录。",
+      "name_exists": "具有此名称的实例已存在",
+      "invalid_name": "无效的实例名称",
+      "invalid_auth": "身份验证失败 - 检查您的API密钥",
+      "invalid_api_key": "无效的API密钥 - 请验证您的凭据",
+      "cannot_connect": "无法连接到API服务",
+      "invalid_model": "所选模型不可用",
+      "rate_limit": "超出速率限制",
+      "context_length": "上下文长度超出限制",
+      "rate_limit_exceeded": "API速率限制超出",
+      "maintenance": "服务正在维护中",
+      "invalid_response": "收到无效的API响应",
+      "api_error": "发生API服务错误",
+      "timeout": "请求超时",
+      "invalid_instance": "指定的实例无效",
+      "unknown": "发生意外错误",
+      "empty": "名称不能为空",
+      "invalid_characters": "名称只能包含字母、数字、空格、下划线和连字符",
+      "name_too_long": "名称必须少于50个字符"
+    },
+    "abort": {
+      "already_configured": "实例已配置"
+    }
+  },
+  "options": {
+    "step": {
+      "init": {
+        "title": "选择提供者",
+        "description": "选择此实例的AI提供者。保存更改后集成将重新加载。",
+        "data": {
+          "api_provider": "API提供者"
+        }
+      },
+      "settings": {
+        "title": "连接和模型设置",
+        "description": "配置API凭据和模型参数。更改将在集成重新加载后生效。",
+        "data": {
+          "api_key": "API密钥",
+          "api_endpoint": "API端点URL",
+          "model": "AI模型",
+          "temperature": "响应创造力（0-2）",
+          "max_tokens": "最大响应长度（1-100000）",
+          "request_interval": "最小请求间隔（0.1-60秒）",
+          "api_timeout": "API请求超时时间（5-600秒）",
+          "context_messages": "要包含在上下文中的先前消息数量（1-20）",
+          "max_history_size": "最大对话历史大小（1-100）",
+          "allow_local_network": "允许本地网络端点（用于自托管代理）"
+        }
+      }
+    }
+  },
+  "selector": {
+    "api_provider": {
+      "options": {
+        "openai": "OpenAI（兼容）",
+        "anthropic": "Anthropic（兼容）",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
+      }
+    }
+  },
+  "services": {
+    "ask_question": {
+      "name": "提问 (HA Text AI)",
+      "description": "向AI模型发送问题并获得详细回答。此服务现在直接返回响应数据，消除了对单独文本传感器的需要和255字符限制。响应也将存储在对话历史中。",
+      "fields": {
+        "instance": {
+          "name": "实例",
+          "description": "要使用的HA文本AI实例名称"
+        },
+        "question": {
+          "name": "问题",
+          "description": "您对AI助手的问题或提示"
+        },
+        "context_messages": {
+          "name": "上下文消息",
+          "description": "要包含在上下文中的先前消息数量（1-20）"
+        },
+        "system_prompt": {
+          "name": "系统提示",
+          "description": "可选的系统提示，用于为此特定问题设置上下文"
+        },
+        "model": {
+          "name": "模型",
+          "description": "选择要使用的AI模型（可选，覆盖默认设置）"
+        },
+        "temperature": {
+          "name": "温度",
+          "description": "控制响应创造力（0.0-2.0）"
+        },
+        "max_tokens": {
+          "name": "最大标记数",
+          "description": "响应的最大长度（1-100000个标记）"
+        },
+        "structured_output": {
+          "name": "结构化输出",
+          "description": "启用JSON结构化输出模式。启用后，AI将以符合提供的模式的有效JSON进行响应。"
+        },
+        "json_schema": {
+          "name": "JSON模式",
+          "description": "定义预期响应结构的JSON模式。启用structured_output时必需。"
+        }
+      }
+    },
+    "clear_history": {
+      "name": "清除历史",
+      "description": "删除对话历史中存储的所有问题和响应",
+      "fields": {
+        "instance": {
+          "name": "实例",
+          "description": "要清除历史的HA文本AI实例名称"
+        }
+      }
+    },
+    "get_history": {
+      "name": "获取历史",
+      "description": "检索对话历史，可选的过滤和排序",
+      "fields": {
+        "instance": {
+          "name": "实例",
+          "description": "要获取历史的HA文本AI实例名称"
+        },
+        "limit": {
+          "name": "限制",
+          "description": "要返回的对话数量（1-100）"
+        },
+        "filter_model": {
+          "name": "过滤模型",
+          "description": "按特定AI模型过滤对话"
+        },
+        "start_date": {
+          "name": "开始日期",
+          "description": "过滤从此日期/时间开始的对话"
+        },
+        "include_metadata": {
+          "name": "包含元数据",
+          "description": "包括额外信息，如使用的标记、响应时间等。"
+        },
+        "sort_order": {
+          "name": "排序顺序",
+          "description": "结果的排序顺序（最新或最旧优先）"
+        }
+      }
+    },
+    "set_system_prompt": {
+      "name": "设置系统提示",
+      "description": "为所有未来的对话设置默认的系统行为指令",
+      "fields": {
+        "instance": {
+          "name": "实例",
+          "description": "要设置系统提示的HA文本AI实例名称"
+        },
+        "prompt": {
+          "name": "系统提示",
+          "description": "定义AI应如何行为和响应的指令"
+        }
+      }
+    }
+  },
+  "entity": {
+    "sensor": {
+      "ha_text_ai": {
+        "name": "{name}",
+        "state": {
+          "ready": "准备就绪",
+          "processing": "处理中",
+          "error": "错误",
+          "disconnected": "已断开连接",
+          "rate_limited": "速率限制",
+          "maintenance": "维护中",
+          "initializing": "初始化中",
+          "retrying": "重试中",
+          "queued": "排队中"
+        },
+        "state_attributes": {
+          "question": {
+            "name": "最后问题"
+          },
+          "response": {
+            "name": "最后响应"
+          },
+          "model": {
+            "name": "当前模型"
+          },
+          "temperature": {
+            "name": "温度"
+          },
+          "max_tokens": {
+            "name": "最大标记数"
+          },
+          "system_prompt": {
+            "name": "系统提示"
+          },
+          "response_time": {
+            "name": "最后响应时间"
+          },
+          "total_responses": {
+            "name": "总响应数"
+          },
+          "error_count": {
+            "name": "错误计数"
+          },
+          "last_error": {
+            "name": "最后错误"
+          },
+          "api_status": {
+            "name": "API状态"
+          },
+          "tokens_used": {
+            "name": "总使用标记数"
+          },
+          "average_response_time": {
+            "name": "平均响应时间"
+          },
+          "last_request_time": {
+            "name": "最后请求时间"
+          },
+          "is_processing": {
+            "name": "处理状态"
+          },
+          "is_rate_limited": {
+            "name": "速率限制状态"
+          },
+          "is_maintenance": {
+            "name": "维护状态"
+          },
+          "api_version": {
+            "name": "API版本"
+          },
+          "endpoint_status": {
+            "name": "端点状态"
+          },
+          "performance_metrics": {
+            "name": "性能指标"
+          },
+          "history_size": {
+            "name": "历史大小"
+          },
+          "uptime": {
+            "name": "正常运行时间"
+          },
+          "total_tokens": {
+            "name": "总标记数"
+          },
+          "prompt_tokens": {
+            "name": "提示标记数"
+          },
+          "completion_tokens": {
+            "name": "完成标记数"
+          },
+          "successful_requests": {
+            "name": "成功请求数"
+          },
+          "failed_requests": {
+            "name": "失败请求数"
+          },
+          "average_latency": {
+            "name": "平均延迟"
+          },
+          "max_latency": {
+            "name": "最大延迟"
+          },
+          "min_latency": {
+            "name": "最小延迟"
+          }
+        }
+      }
+    }
+  }
+}

custom_components/ha_text_ai/utils.py

@@ -0,0 +1,125 @@
+"""
+Utility functions for HA Text AI integration.
+
+@license: PolyForm Noncommercial 1.0.0 (https://polyformproject.org/licenses/noncommercial/1.0.0)
+@author: SMKRV
+@github: https://github.com/smkrv/ha-text-ai
+@source: https://github.com/smkrv/ha-text-ai
+"""
+from __future__ import annotations
+
+import ipaddress
+import socket
+from typing import Any
+from urllib.parse import urlparse
+
+from homeassistant.const import CONF_API_KEY
+from homeassistant.core import HomeAssistant
+
+
+def normalize_name(name: str) -> str:
+    """Normalize name to conform to HA naming convention using underscores."""
+    normalized = ''.join(c if c.isalnum() or c == '_' else '_' for c in name)
+    normalized = '_'.join(filter(None, normalized.split('_')))
+    return normalized.lower()
+
+
+def safe_log_data(
+    data: dict[str, Any],
+    sensitive_keys: tuple[str, ...] = (CONF_API_KEY,),
+) -> dict[str, Any]:
+    """Filter sensitive keys from data for safe logging."""
+    return {k: "***" if k in sensitive_keys else v for k, v in data.items()}
+
+
+class _RestrictedIPError(ValueError):
+    """Raised when an IP address is in a restricted range."""
+
+
+def _check_ip_restricted(addr: ipaddress.IPv4Address | ipaddress.IPv6Address) -> bool:
+    """Check if an IP address is in a restricted range."""
+    return (
+        addr.is_private
+        or addr.is_reserved
+        or addr.is_loopback
+        or addr.is_link_local
+        or addr.is_multicast
+        or addr.is_unspecified
+    )
+
+
+async def validate_endpoint(hass: HomeAssistant, endpoint: str, *, allow_local: bool = False) -> str:
+    """Validate API endpoint URL for security.
+
+    Ensures HTTPS-only and blocks private/reserved IP ranges (SSRF protection).
+    When allow_local is True, permits private IPs and HTTP scheme for self-hosted proxies.
+    Uses async DNS resolution to avoid blocking the event loop.
+    Returns the validated endpoint stripped of trailing slashes.
+
+    Raises:
+        ValueError: If the endpoint fails validation.
+    """
+    parsed = urlparse(endpoint)
+
+    if allow_local:
+        if parsed.scheme not in ("https", "http"):
+            raise ValueError("Only HTTPS and HTTP endpoints are allowed")
+    else:
+        if parsed.scheme not in ("https",):
+            raise ValueError("Only HTTPS endpoints are allowed")
+
+    hostname = parsed.hostname
+    if not hostname:
+        raise ValueError("Invalid endpoint URL: no hostname")
+
+    if allow_local:
+        # Even in local mode, block multicast and unspecified addresses
+        _is_ip_literal = True
+        try:
+            addr = ipaddress.ip_address(hostname)
+        except ValueError:
+            _is_ip_literal = False
+
+        if _is_ip_literal:
+            if addr.is_multicast or addr.is_unspecified:
+                raise ValueError("Multicast and unspecified addresses are not allowed")
+        else:
+            # Not an IP literal — resolve and check
+            try:
+                addrinfos = await hass.async_add_executor_job(
+                    socket.getaddrinfo, hostname, None
+                )
+                for family, _type, _proto, _canonname, sockaddr in addrinfos:
+                    resolved = ipaddress.ip_address(sockaddr[0])
+                    if resolved.is_multicast or resolved.is_unspecified:
+                        raise ValueError(
+                            "Hostname resolves to multicast/unspecified address"
+                        )
+            except socket.gaierror as err:
+                raise ValueError(f"Cannot resolve hostname: {hostname}") from err
+    else:
+        # Full SSRF protection — block private/reserved IPs
+        try:
+            addr = ipaddress.ip_address(hostname)
+            if _check_ip_restricted(addr):
+                raise _RestrictedIPError("Private/reserved IP addresses are not allowed")
+        except _RestrictedIPError:
+            raise
+        except ValueError:
+            # Not an IP literal — resolve hostname and check all resolved IPs
+            # to prevent DNS rebinding attacks
+            try:
+                addrinfos = await hass.async_add_executor_job(
+                    socket.getaddrinfo, hostname, None
+                )
+                for family, _type, _proto, _canonname, sockaddr in addrinfos:
+                    ip_str = sockaddr[0]
+                    resolved_addr = ipaddress.ip_address(ip_str)
+                    if _check_ip_restricted(resolved_addr):
+                        raise ValueError(
+                            "Hostname resolves to a restricted IP range"
+                        )
+            except socket.gaierror as err:
+                raise ValueError(f"Cannot resolve hostname: {hostname}") from err
+
+    return endpoint.rstrip("/")

hacs.json

@@ -1,5 +1,5 @@
 {
-  "name": "HA text AI",
+  "name": "HA Text AI",
   "render_readme": true,
-  "homeassistant": "2024.11.0"
+  "homeassistant": "2024.12.0"
 }

structure.md

@@ -1,25 +1,35 @@
 ```
-ha-text-ai/
-│
-├── custom_components/
-    ├── ha_text_ai/
-    │   ├── __init__.py
-    │   ├── config_flow.py
-    │   ├── coordinator.py
-    │   ├── manifest.json
-    │   ├── sensor.py
-    │   ├── services.yaml
-    │   ├── const.py
-    │   └── api_client.py
-    │
-    ├── translations/
-    │   ├── en.json
-    │   ├── de.json
-    │   └── ru.json
-    │
-    └── icons/
-        ├── icon.png
-        ├── icon@2x.png
-        ├── logo.png
-        └── logo@2x.png
+custom_components/ha_text_ai/
+├── __init__.py
+├── api_client.py
+├── config_flow.py
+├── const.py
+├── coordinator.py
+├── history.py
+├── metrics.py
+├── providers.py
+├── sensor.py
+├── services.yaml
+├── strings.json
+├── utils.py
+├── icons
+│   ├── dark_icon.png
+│   ├── dark_icon@2x.png
+│   ├── dark_logo.png
+│   ├── dark_logo@2x.png
+│   ├── icon.png
+│   ├── icon@2x.png
+│   ├── logo.png
+│   └── logo@2x.png
+├── manifest.json
+└── translations
+    ├── de.json
+    ├── en.json
+    ├── es.json
+    ├── hi.json
+    ├── it.json
+    ├── ru.json
+    ├── sr.json
+    └── zh.json
+
 ```